Steven Holl – Ideas on Architecture

Steven Hold: Thoughts and Ideas on Architecture As I sit and listen to the rainfall, I can't help but wonder about the changing of seasons. Winter to spring, Spring to summer, summer to fall, fall back to winter. While each droplet of rain must have Journeyed long and far before it descended upon me, now it's Just a pool of droplets. The best part about spring is the rain showers. Without the spring rain we would have no summer flowers, no gardens, not leaves or grass. Spring marks the direction of a new change. One with more life, a new beginning of sorts.A precedent for the following months; a metamorphosis from en season to the next. Ancient Greek philosopher Heraclites found fascination with change in its most simple form. He believed that all is flux, and nothing stays still. But what if he was wrong†¦? If flux could be stopped what would happen? I could walk out into this storm and not be wet, for the droplets of rain are still and the clouds stationary. In the moment, I f ind tranquility in the storm. Peacefulness rests among the stillness of earth. Not a splash of water made, not a scour in the trees, not even a whistle to the wind.I think to myself, ‘The serenity of nature is unlike anything else in this world. All of a sudden, CLASH!! Lightning strikes followed by a violent boom of thunder. A nearby tree creaks like an old door opening as it falls to the ground; unexpectedly I became drenched by the rainfall. I sought shelter under a roof, but it seemed as if the world was at ease. Almost as if earth made a treaty with itself to remain motionless for the rest of time. Nevertheless, it wasn't because if nothing changed then storms wouldn't occur, seasons couldn't transpire, and life would be lifeless.This earth we live on is one of a kind and distinct from anything else. Earth speaks for itself and Heraclites states this excellently, â€Å"Not l, but the world says it all: All is one. And yet everything comes in season. † In comparison to the precedent of spring to the rest of the seasons, a person's early life can shape the following years in their life. Steven Holly's career was foreshadowed by his earliest years when he and his brother built a 3-story tree house and also an underground clubhouse. This was not only outlined in his childhood, but also in his years of education.While growing up in Beaverton, Washington, he developed the desire to make things, sculpt, draw, and build. After high school, Hold went to study architecture at the University of Washington. His Junior year he left the states and engulfed himself in the great city of Rome, moving from Beaverton, a shipyard city with little architectural density, to Rome, the pinnacle of architectural history. 5 While in Rome, the Vietnam War was taking place so, Hold, instead of developing his thoughts and ideas on architecture, wrote a conscientious objection on philosophical rather religious grounds.After receiving a reply, he was dismissed due to †Å"physical deformity' and never had an actual physical examination. Hold obsessed over his objection because he didn't want them to falsify his opposition, and consequently left Rome with no projects. Upon returning to Washington, he had difficulties finding a firm to hire him. After a year at a small firm outside of Seattle, Hold left to go to San Francisco, where he formed a union with William Stout and Bill Zimmerman; they called themselves â€Å"Opus 411 . † Together they entered competitions and wrote declarations of architecture, but all ended too soon, for Hold was broke and needed a job.In search of a Job and possible graduate school, Hold was accepted at Harvard, Princeton, and Yale, and on top of that hired at Louis Khan's office in Philadelphia. He made the decision to take the Job and decline the schools. Confusion descended once Hold received word of Khan's death. He declined acceptance to graduate schools for a Job that was no longer possible. Fortunately in 197 6, Hold was offered to study, tuition free, at the Architectural Association in London by a man named Alvin Boyar. 5 For that reason, Hold made another life-changing decision and packed his bags to go to London.During that time he traveled to every possible building to experience them first hand and to sketch hem. Living as a vagabond in the streets of London, by some meaner, Hold managed to find a client from Paris. His new client was upset with his noisy and crowded vacation home so Hold made large pencil drawings of a new retreat house offset from the shore. In one of the drawings he sketched a man on a boat headed to his refuge home, his back to the home and face to the shore. Hold comments, â€Å"The character in the rowboat illustrates the way that all of us must work. He cannot see where he is going, only where he has been.Progress is tempered by a sense of mystery, of doubt. â€Å"5 A couple years later, Hold made more elaborate pencil drawings of a project for the South B ronx called Gymnasium-Bridge. This project won a Progressive Architecture Award in 1978. Upset by the way his work was presented in Progressive Architecture, he called his colleague Bill Stout, who had opened a bookstore back in San Francisco to make a publication of manifestos and single projects. This was the inception of what would be known as â€Å"Pamphlet Architecture. † Hold set specific guidelines for him and his colleagues to follow for this publication.This was an avian-garden idea at the mime and gave new and unusual ways of looking at architecture. These anthologies feature groundbreaking works by forward thinkers of today's most well-known architects, including Steven Hold, Living Timidity, Lubbers Woods, and Gaza Had. 6/7 Holly's excerpts from Pamphlet Architecture are very much concerned with typology and morphology, that is, a study based on classification and also a study on building forms. â€Å"Pamphlet Architecture #5 The Alphabetical City' speaks on the nature of urban buildings during the first half of the 20th century.Hold inscribes, â€Å"†¦ The notorious portions of cities that evolved on gridiron plans – certain letter-like buildings recurred. The â€Å"L†, or the â€Å"l† type depend on their adjoining structures for meaning. They become â€Å"dead letters† when left stranded as free- standing buildings. â€Å"6 You can see here Hold had been analyzing buildings and then classifying certain buildings by the letter in the alphabet they resembled. The forms of these buildings from the generation before him caused him to questions the idea of architecture from that time.Holly's current language of architecture wasn't uncovered until he came across the arks of French philosopher Maurice Merle-Pointy in 1984. 1 This was a time when Hold radically changed his methods for making and understanding architecture. Subsequent to the discovery of Merle-Pointy, Hold brought light to the idea of deriving p rojects from concepts outside of architecture. Over the years, he harnessed this method and played with it as a departure for his work. From there on out, Hold became preoccupied with the idea of experience.Merle-Pointy expresses, â€Å"We know not through our intellect but through our experience. â€Å"3 The phenomenology that Merle-Pointy writes about is what Hold achieves in his architecture. While most architects work outside-in, Hold takes an opposite stance and works inside-out because he affirms that, â€Å"space is the incredible media of architecture. â€Å"8 It is an extraordinary responsibility to be an architect because the buildings we make are for people to use. Hold understands this and attempts to make people perceive space differently, to make something visible that they normally wouldn't.A work of his that exemplifies this is the Chapel of SST. Igniting in Seattle, Washington. In this project, Hold starts with the concept, ‘Seven Bottles of Light in a Sto ne Box. ‘ Each of the openings for light allow the sunlight to reflect off colored walls in a way that causes a conversion to colored-light. You can imagine being in the space that funnels colors at you making light ever more noticeable. This making of architecture relates building, site, and situation with body, space, time, light, and movement. 4 Holly's buildings really execute the interaction between architecture and phenomenology.It doesn't come as a surprise that Holly's major preoccupation is the phenomena of light. We live in a world that we know through vision, which can only be possible with the help of light. The dynamic of light defines several of Holly's works including: Writing With Light House, Porosity House, Sun Slice House, Kinsman Museum of Contemporary Art, Nelson-Atkins Museum of Art Addition, Chapel of SST. Igniting, Museum of the City, and NYU School of Philosophy. 2 However, these projects epitomize his thoughts on light, all of his works constitute and deal with light.Specifically in Writing with Light House, Hold inscribes light in such a way that celebrates light and, its counterpart, shadow. Strips of white light coat the interior in accord with the time of day and season. Shadows then become the strokes on the reface causing an ornament of pattern. This strategy shapes light that really gives it meaning and insight. It's not only the approach on light that makes his architecture original, but also his ability to take familiar ideas and transform them into something new. It wasn't until the ass's when Hold started consistently getting things built.Part of the struggle in his career was becoming famous. This can be challenging because of cruel criticisms or lack of attention towards your work. For Hold, it wasn't until after his Pace Collection Showroom in New York, that he received a world-known status. He was given a huge amount of critical attention in New York, Europe, and Asia for his new and fresh take on modernism. It to ok a couple decades but he now has work in Italy, Germany, France, Japan, Finland, Switzerland, Norway, The Netherlands, Denmark, and China.Holly's originality that is known throughout the world is, in part, due to his way of thinking and developing ideas. It is not necessarily theories of architecture that shaped this, but phenomenology and science that have shaped his beliefs and ideas. Ideas are very important to Hold and this is where he derives his inspiration. He is fearless when it comes to addressing the world at large for inspiration in his projects. Many shy away because of the criticisms one will face for fear of comparison to larger ideas, nevertheless Hold is audacious.Audacity is exactly what Hold advocates. He encourages students to question everything and this is one reason he, himself, has become such a success. 8 One part of being an architect is that you must be able to fluently articulate your personal thoughts and ideas, rather than simply following the ideas of someone else. It then becomes a push for what you think should happen. Holly's character is tested when working with clients because he must be uncompromising and demanding if he wants to pursue the realization of his concepts. His self-assurance comes from the knowledge of himself.He has never had any doubts on who he is and what he wants to accomplish, and this has lead to his triumph. Even though he must be adamant and resolute, he has sought criticism from respected colleagues and peers of his designs over the years. 2 Hold discusses their commentary and evaluation with them after overcoming his incredulity. This is a testament of his respect for other people's opinions and ideas. It also reveals his wisdom in seeking out honest and tough critiques. This may be the reason he is able to keep his knife so sharp.Without the help of others, he would become dull, thereby making his architecture banal. This essay ends with a glimpse of the way Steven Hold sees architecture for the 21 st Century. Hold was born in 1947. He lived in latter half of the 20th Century; he saw and helped change the way architecture is defined today. He truly sees how architecture has been grounded by the physical aspects of having limited resources in the past, to the increasing technological ways in which we can now build. The constructive ramification into modern life and new ways of seeing are vital traits he believes today's architects must have.Hold elaborates, â€Å"Any architect caught up with the current speed of globalization of today's architecture realizes that this is an unprecedented time in the history of architecture: requiring an unprecedented philosophical commitment. † He continues, â€Å"†¦ The challenge of extremely diverse lands, cultures, and climates and their urban conditions set unparalleled obligations for architecture today†¦ A theory reversing specific to universal – a black swan theory – suggests an aim for larger, more comple x building types.A twenty-first century position that strives to airframe the inherited dualism of the last century's suffixes might spark a new paradigm shift toward a new focus on architecture's potential to shape experience, interrelating body, brain and world. â€Å"3 A new generation will emerge after the passing of Steven Hold, one inspired by the books and buildings he bequeathed to humanity. For now, Hold will continue to be a leading architect in the world. It's a great field to be apart of with myriad possibilities, and it is my hope to one day be given the chance to make a richer environment and Join the field of architecture.

User Authentication Through Mouse Dynamics

16 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 User Authentication Through Mouse Dynamics Chao Shen, Student Member, IEEE, Zhongmin Cai, Member, IEEE, Xiaohong Guan, Fellow, IEEE, Youtian Du, Member, IEEE, and Roy A. Maxion, Fellow, IEEE Abstract—Behavior-based user authentication with pointing devices, such as mice or touchpads, has been gaining attention. As an emerging behavioral biometric, mouse dynamics aims to address the authentication problem by verifying computer users on the basis of their mouse operating styles.This paper presents a simple and ef? cient user authentication approach based on a ? xed mouse-operation task. For each sample of the mouse-operation task, both traditional holistic features and newly de? ned procedural features are extracted for accurate and ? ne-grained characterization of a user’s unique mouse behavior. Distance-measurement and eigenspace-transformation techniques are applied to obtain featur e components for ef? ciently representing the original mouse feature space.Then a one-class learning algorithm is employed in the distance-based feature eigenspace for the authentication task. The approach is evaluated on a dataset of 5550 mouse-operation samples from 37 subjects. Extensive experimental results are included to demonstrate the ef? cacy of the proposed approach, which achieves a false-acceptance rate of 8. 74%, and a false-rejection rate of 7. 69% with a corresponding authentication time of 11. 8 seconds. Two additional experiments are provided to compare the current approach with other approaches in the literature.Our dataset is publicly available to facilitate future research. Index Terms—Biometric, mouse dynamics, authentication, eigenspace transformation, one-class learning. I. INTRODUCTION T HE quest for a reliable and convenient security mechanism to authenticate a computer user has existed since the inadequacy of conventional password mechanism was reali zed, ? rst by the security community, and then gradually by the Manuscript received March 28, 2012; revised July 16, 2012; accepted September 06, 2012. Date of publication October 09, 2012; date of current version December 26, 2012.This work was supported in part by the NSFC (61175039, 61103240, 60921003, 60905018), in part by the National Science Fund for Distinguished Young Scholars (60825202), in part by 863 High Tech Development Plan (2007AA01Z464), in part by the Research Fund for Doctoral Program of Higher Education of China (20090201120032), and in part by Fundamental Research Funds for Central Universities (2012jdhz08). The work of R. A. Maxion was supported by the National Science Foundation under Grant CNS-0716677. Any opinions, ? dings, conclusions, or recommendations expressed in this material are those of the authors, and do not necessarily re? ect the views of the National Science Foundation. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Sviatoslav Voloshynovskiy. C. Shen, Z. Cai, X. Guan, and Y. Du are with the MOE Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China (e-mail: [email  protected] xjtu. edu. cn; [email  protected] xjtu. edn. cn; [email  protected] xjtu. edu. cn; [email  protected] jtu. edu. cn). R. A. Maxion is with the Dependable Systems Laboratory, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: [email  protected] cmu. edu). Color versions of one or more of the ? gures in this paper are available online at http://ieeexplore. ieee. org. Digital Object Identi? er 10. 1109/TIFS. 2012. 2223677 public [31]. As data are moved from traditional localized computing environments to the new Cloud Computing paradigm (e. g. , Box. net and Dropbox), the need for better authentication has become more pressing.Recently, several large-scale password leakages exposed users to an unprecedented risk of disclosure and abuse of their information [47], [48]. These incidents seriously shook public con? dence in the security of the current information infrastructure; the inadequacy of password-based authentication mechanisms is becoming a major concern for the entire information society. Of various potential solutions to this problem, a particularly promising technique is mouse dynamics. Mouse dynamics measures and assesses a user’s mouse-behavior characteristics for use as a biometric.Compared with other biometrics such as face, ? ngerprint and voice [20], mouse dynamics is less intrusive, and requires no specialized hardware to capture biometric information. Hence it is suitable for the current Internet environment. When a user tries to log into a computer system, mouse dynamics only requires her to provide the login name and to perform a certain sequence of mouse operations. Extracted behavioral features, based on mouse movements and clicks, are compared to a legitimate user’s pro? le. A match authenticates the user; otherwise her access is denied.Furthermore, a user’s mouse-behavior characteristics can be continually analyzed during her subsequent usage of a computer system for identity monitoring or intrusion detection. Yampolskiy et al. provide a review of the ? eld [45]. Mouse dynamics has attracted more and more research interest over the last decade [2]–[4], [8], [14]–[17], [19], [21], [22], [33], [34], [39]–[41], [45], [46]. Although previous research has shown promising results, mouse dynamics is still a newly emerging technique, and has not reached an acceptable level of performance (e. . , European standard for commercial biometric technology, which requires 0. 001% false-acceptance rate and 1% false-rejection rate [10]). Most existing approaches for mouse-dynamics-based user authentication result in a low authentication accuracy or an unreasonably long authenticatio n time. Either of these may limit applicability in real-world systems, because few users are willing to use an unreliable authentication mechanism, or to wait for several minutes to log into a system.Moreover, previous studies have favored using data from real-world environments over experimentally controlled environments, but this realism may cause unintended side-effects by introducing confounding factors (e. g. , effects due to different mouse devices) that may affect experimental results. Such confounds can make it dif? cult to attribute experimental outcomes solely to user behavior, and not to other factors along the long path of mouse behavior, from hand to computing environment [21], [41]. 1556-6013/$31. 00  © 2012 IEEE SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 17It should be also noted that most mouse-dynamics research used data from both the impostors and the legitimate user to train the classi? cation or detection model. However, in the scenario of mouse-d ynamics-based user authentication, usually only the data from the legitimate user are readily available, since the user would choose her speci? c sequence of mouse operations and would not share it with others. In addition, no datasets are published in previous research, which makes it dif? cult for third-party veri? cation of previous work and precludes objective comparisons between different approaches.A. Overview of Approach Faced with the above challenges, our study aims to develop a mouse-dynamics-based user authentication approach, which can perform user authentication in a short period of time while maintaining high accuracy. By using a controlled experimental environment, we have isolated inherent behavioral characteristics as the primary factors for mouse-behavior analysis. The overview of the proposed approach is shown in Fig. 1. It consists of three major modules: (1) mouse-behavior capture, (2) feature construction, and (3) training/classi? cation. The ? st module serves to create a mouse-operation task, and to capture and interpret mouse-behavior data. The second module is used to extract holistic and procedural features to characterize mouse behavior, and to map the raw features into distance-based features by using various distance metrics. The third module, in the training phase, applies kernel PCA on the distance-based feature vectors to compute the predominant feature components, and then builds the user’s pro? le using a one-class classi? er. In the classi? cation phase, it determines the user’s identity using the trained classi? r in the distance-based feature eigenspace. B. Purpose and Contributions of This Paper This paper is a signi? cant extension of an earlier and much shorter version [40]. The main purpose and major contributions of this paper are summarized as follows: †¢ We address the problem of unintended side-effects of inconsistent experimental conditions and environmental variables by restricting usersâ€℠¢ mouse operations to a tightly-controlled environment. This isolates inherent behavioral characteristics as the principal factors in mouse behavior analysis, and substantially reduces the effects of external confounding factors. Instead of the descriptive statistics of mouse behaviors usually adopted in existing work, we propose newly-de? ned procedural features, such as movement speed curves, to characterize a user’s unique mouse-behavior characteristics in an accurate and ? ne-grained manner. These features could lead to a performance boost both in authentication accuracy and authentication time. †¢ We apply distance metrics and kernel PCA to obtain a distance-based eigenspace for ef? ciently representing the original mouse feature space.These techniques partially handle behavioral variability, and make our proposed approach stable and robust to variability in behavior data. †¢ We employ one-class learning methods to perform the user authentication task, so that the detection model is Fig. 1. Overview of approach. built solely on the data from the legitimate user. One-class methods are more suitable for mouse-dynamics-based user authentication in real-world applications. †¢ We present a repeatable and objective evaluation procedure to investigate the effectiveness of our proposed approach through a series of experiments.As far as we know, no earlier work made informed comparisons between different features and results, due to the lack of a standard test protocol. Here we provide comparative experiments to further examine the validity of the proposed approach. †¢ A public mouse-behavior dataset is established (see Section III for availability), not only for this study but also to foster future research. This dataset contains high-quality mouse-behavior data from 37 subjects. To our knowledge, this study is the ? rst to publish a shared mouse-behavior dataset in this ? eld. This study develops a mouse-dynamics-based user authenticat ion approach that performs user authentication in a short time while maintaining high accuracy. It has several desirable properties: 1. it is easy to comprehend and implement; 2. it requires no specialized hardware or equipment to capture the biometric data; 3. it requires only about 12 seconds of mouse-behavior data to provide good, steady performance. The remainder of this paper is organized as follows: Section II describes related work. Section III presents a data-collection process. Section IV describes the feature-construction process.Section V discusses the classi? cation techniques for mouse dynamics. Section VI presents the evaluation methodology. Section VII presents and analyzes experimental results. Section VIII offers a discussion and possible extensions of the current work. Finally, Section IX concludes. 18 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 II. BACKGROUND AND RELATED WORK In this section, we provide background on mouse- dynamics research, and various applications for mouse dynamics (e. g. , authentication versus intrusion detection).Then we focus on applying mouse dynamics to user authentication. A. Background of Mouse Dynamics Mouse dynamics, a behavioral biometric for analyzing behavior data from pointing devices (e. g. , mouse or touchpad), provides user authentication in an accessible and convenient manner [2]–[4], [8], [14]–[17], [19], [21], [22], [33], [34], [39]–[41], [45], [46]. Since Everitt and McOwan [14] ? rst investigated in 2003 whether users could be distinguished by the use of a signature written by mouse, several different techniques and uses for mouse dynamics have been proposed.Most researchers focus on the use of mouse dynamics for intrusion detection (sometimes called identity monitoring or reauthentication), which analyzes mouse-behavior characteristics throughout the course of interaction. Pusara and Brodley [33] proposed a reauthentication scheme using m ouse dynamics for user veri? cation. This study presented positive ? ndings, but cautioned that their results were only preliminary. Gamboa and Fred [15], [16] were some of the earliest researchers to study identity monitoring based on mouse movements.Later on, Ahmed and Traore [3] proposed an approach combining keystroke dynamics with mouse dynamics for intrusion detection. Then they considered mouse dynamics as a standalone biometric for intrusion detection [2]. Recently, Zheng et al. [46] proposed angle-based metrics of mouse movements for reauthentication systems, and explored the effects of environmental factors (e. g. , different machines). Yet only recently have researchers come to the use of mouse dynamics for user authentication (sometimes called static authentication), which analyzes mouse-behavior characteristics at particular moments.In 2007, Gamboa et al. [17] extended their approaches in identity monitoring [15], [16] into web-based authentication. Later on, Kaminsky e t al. [22] presented an authentication scheme using mouse dynamics for identifying online game players. Then, Bours and Fullu [8] proposed an authentication approach by requiring users to make use of the mouse for tracing a maze-like path. Most recently, a full survey of the existing work in mouse dynamics pointed out that mouse-dynamics research should focus on reducing authentication time and taking the effect of environmental variables into account [21]. B.User Authentication Based on Mouse Dynamics The primary focus of previous research has been on the use of mouse dynamics for intrusion detection or identity monitoring. It is dif? cult to transfer previous work directly from intrusion detection to authentication, however, because a rather long authentication period is typically required to collect suf? cient mouse-behavior data to enable reasonably accurate veri? cation. To our knowledge, few papers have targeted the use of mouse dynamics for user authentication, which will be the central concern of this paper. Hashia et al. [19] and Bours et al. 8] presented some preliminary results on mouse dynamics for user authentication. They both asked participants to perform ? xed sequences of mouse operations, and they analyzed behavioral characteristics of mouse movements to authenticate a user during the login stage. Distance-based classi? ers were established to compare the veri? cation data with the enrollment data. Hashia et al. collected data from 15 participants using the same computer, while Bours et al. collected data from 28 subjects using different computers; they achieved equal-error rates of 15% and 28% respectively.Gamboa et al. [17] presented a web-based user authentication system based on mouse dynamics. The system displayed an on-screen virtual keyboard, and required users to use the mouse to enter a paired username and pin-number. The extracted feature space was reduced to a best subspace through a greedy search process. A statistical model based on the Weibull distribution was built on training data from both legitimate and impostor users. Based on data collected from 50 subjects, the researchers reported an equal-error rate of 6. 2%, without explicitly reporting authentication time.The test data were also used for feature selection, which may lead to an overly optimistic estimate of authentication performance [18]. Recently, Revett et al. [34] proposed a user authentication system requiring users to use the mouse to operate a graphical, combination-lock-like GUI interface. A small-scale evaluation involving 6 subjects yielded an average false-acceptance rate and false-rejection rate of around 3. 5% and 4% respectively, using a distance-based classi? er. However, experimental details such as experimental apparatus and testing procedures were not explicitly reported. Aksari et al. 4] presented an authentication framework for verifying users based on a ? xed sequence of mouse movements. Features were extracted from nine move ments among seven squares displayed consecutively on the screen. They built a classi? er based on scaled Euclidean distance using data from both legitimate users and impostors. The researchers reported an equal-error rate of 5. 9% over 10 users’ data collected from the same computer, but authentication time was not reported. It should be noted that the above two studies were performed on a small number of users—only 6 users in [34], and 10 users in [4]—which may be insuf? ient to evaluate de? nitively the performance of these approaches. The results of the above studies have been mixed, possibly due to the realism of the experiments, possibly due to a lack of real differences among users, or possibly due to experimental errors or faulty data. A careful reading of the literature suggests that (1) most approaches have resulted in low performance, or have used a small number of users, but since these studies do not tend to be replicated, it is hard to pin the discr epancies on any one thing; (2) no research group provided a shared dataset.In our study, we control the experimental environment to increase the likelihood that our results will be free from experimental confounding factors, and we attempt to develop a simple and ef? cient user authentication approach based on mouse dynamics. We also make our data available publicly. III. MOUSE DATA ACQUISITION In this study, we collect mouse-behavior data in a controlled environment, so as to isolate behavioral characteristics as the principal factors in mouse behavior analysis. We offer here SHEN et al. USER AUTHENTICATION THROUGH MOUSE DYNAMICS 19 considerable detail regarding the conduct of data collection, because these particulars can best reveal potential biases and threats to experimental validity [27]. Our data set is available 1. A. Controlled Environment In this study, we set up a desktop computer and developed a Windows application as a uniform hardware and software platform for the coll ection of mouse-behavior data. The desktop was an HP workstation with a Core 2 Duo 3. 0 GHz processor and 2 GB of RAM.It was equipped with a 17 HP LCD monitor (set at 1280 1024 resolution) and a USB optical mouse, and ran the Windows XP operating system. Most importantly, all system parameters relating to the mouse, such as speed and sensitivity con? gurations, were ? xed. The Windows application, written in C#, prompted a user to conduct a mouse-operation task. During data collection, the application displayed the task in a full-screen window on the monitor, and recorded (1) the corresponding mouse operations (e. g. , mouse-single-click), (2) the positions at which the operations occurred, and (3) the timestamps of the operations.The Windows-event clock was used to timestamp mouse operations [28]; it has a resolution of 15. 625 milliseconds, corresponding to 64 updates per second. When collecting data, each subject was invited to perform a mouse-operations task on the same desktop computer free of other subjects; data collection was performed one by one on the same data-collection platform. These conditions make hardware and software factors consistent throughout the process of data collection over all subjects, thus removing unintended side-effects of unrelated hardware and software factors. B.Mouse-Operation Task Design To reduce behavioral variations due to different mouse-operation sequences, all subjects were required to perform the same sequence of mouse operations. We designed a mouse-operation task, consisting of a ? xed sequence of mouse operations, and made these operations representative of a typical and diverse combination of mouse operations. The operations were selected according to (1) two elementary operations of mouse clicks: single click and double click; and (2) two basic properties of mouse movements: movement direction and movement distance [2], [39].As shown in Fig. 2, movement directions are numbered from 1 to 8, and each of them is sel ected to represent one of eight 45-degree ranges over 360 degrees. In addition, three distance intervals are considered to represent short-, middle- and long-distance mouse movements. Table I shows the directions and distances of the mouse movements used in this study. During data collection, every two adjacent movements were separated by either a single click or a double click. As a whole, the designed task consists of 16 mouse movements, 8 single clicks, and 8 double clicks.It should be noted that our task may not be unique. However, the task was carefully chosen to induce users to perform a wide variety of mouse movements and clicks that were both typical and diverse in an individual’s repertoire of daily mouse behaviors. 1The mouse-behavior dataset is available from: http://nskeylab. xjtu. edu. cn/ projects/mousedynamics/behavior-data-set/. Fig. 2. Mouse movement directions: sector 1 covers all operations performed degrees and degrees. with angles between TABLE I MOUSE MO VEMENTS IN THE DESIGNED MOUSE-OPERATION TASK C.Subjects We recruited 37 subjects, many from within our lab, but some from the university at large. Our sample of subjects consisted of 30 males and 7 females. All of them were right-handed users, and had been using a mouse for a minimum of two years. D. Data-Collection Process All subjects were required to participate in two rounds of data collection per day, and waited at least 24 hours between collections (ensuring that some day-to-day variation existed within the data). In each round, each subject was invited, one by one, to perform the same mouse-operation task 10 times.A mouse-operation sample was obtained when a subject performed the task one time, in which she ? rst clicked a start button on the screen, then moved the mouse to click subsequent buttons prompted by the data-collection application. Additionally, subjects were instructed to use only the external mouse device, and they were advised that no keyboard would be needed. S ubjects were told that if they needed a break or needed to stretch their hands, they were to do so after they had accomplished a full round. This was intended to prevent arti? cially anomalous mouse operations in the middle of a task.Subjects were admonished to focus on the task, as if they were logging into their own accounts, and to avoid distractions, such as talking with the experimenter, while the task was in progress. Any error in the operating process (e. g. , single-clicking a button when requiring double-clicking it) caused the current task to be reset, requiring the subject to redo it. 20 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 TABLE II MOUSE DYNAMICS FEATURES Subjects took between 15 days and 60 days to complete data collection.Each subject accomplished 150 error-free repetitions of the same mouse-operation task. The task took between 6. 2 seconds and 21. 3 seconds, with an average of 11. 8 seconds over all subjects. The ? nal dataset contained 5550 samples from 37 subjects. IV. FEATURE CONSTRUCTION In this section, we ? rst extract a set of mouse-dynamics features, and then we use distance-measurement methods to obtain feature-distance vectors for reducing behavioral variability. Next, we utilize an eigenspace transformation to extract principal feature components as classi? er input. A.Feature Extraction The data collected in Section III are sequences of mouse operations, including left-single-clicks, left-double-clicks, and mouse-movements. Mouse features were extracted from these operations, and were typically organized into a vector to represent the sequence of mouse operations in one execution of the mouse-operation task. Table II summarizes the derived features in this study. We characterized mouse behavior based on two basic types of mouse operations—mouse click and mouse movement. Each mouse operation was then analyzed individually, and translated into several mouse features.Our study divi ded these features into two categories: †¢ Holistic features: features that characterize the overall properties of mouse behaviors during interactions, such as single-click and double-click statistics; †¢ Procedural features: features that depict the detailed dynamic processes of mouse behaviors, such as the movement speed and acceleration curves. Most traditional features are holistic features, which suf? ce to obtain a statistical description of mouse behavior, such as the mean value of click times. They are easy to compute and comprehend, but they only characterize general attributes of mouse behavior.In our study, the procedural features characterize in-depth procedural details of mouse behavior. This information more accurately re? ects the ef? ciency, agility and motion habits of individual mouse users, and thus may lead to a performance boost for authentication. Experimental results in Section VII demonstrate the effectiveness of these newly-de? ned features. B. Dis tance Measurement The raw mouse features cannot be used directly by a classi? er, because of high dimensionality and behavioral variability. Therefore, distance-measurement methods were applied to obtain feature-distance vectors and to mitigate the effects of these issues.In the calculation of distance measurement, we ? rst used the Dynamic Time Warping (DTW) distance [6] to compute the distance vector of procedural features. The reasons for this choice are that (1) procedural features (e. g. , movement speed curve) of two data samples are not likely to consist of the exactly same number of points, whether these samples are generated by the same or by different subjects; (2) DTW distance can be applied directly to measure the distance between the procedural features of two samples without deforming either or both of the two sequences in order to get an equal number of points.Next, we applied Manhattan distance to calculate the distance vector of holistic features. The reasons for th is choice are that (1) this distance is independent between dimensions, and can preserve physical interpretation of the features since its computation is the absolute value of cumulative difference; (2) previous research in related ? elds (e. g. , keystroke dynamics) reported that the use of Manhattan distance for statistical features could lead to a better performance [23]. ) Reference Feature Vector Generation: We established the reference feature vector for each subject from her training feature vectors. Let , be the training set of feature vectors for one subject, where is a -dimensional mouse feature vector extracted from the th training sample, and is the number of training samples. Consider how the reference feature vector is generated for each subject: Step 1: we computed the pairwise distance vector of procedural features and holistic features between all pairs of training feature vectors and .We used DTW distance to calculate the distance vector of procedural features for measuring the similarity between the procedural components of the two feature vectors, and we applied Manhattan distance to calculate the distance vector of holistic features . (1) where , and represents the procedural components of represents the holistic components. SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 21 Step 2: we concatenated the distance vectors of holistic features and procedural features together to obtain a distance vector for the training feature vectors and by (2) Step 3: we normalized vector: to get a scale-invariant feature nd sample covariance . Then we can obtain the mean of such a training set by (5) (6) (3) is the mean of all where pairwise distance vectors from the training set, and is the corresponding standard deviation. Step 4: for each training feature vector, we calculated the arithmetic mean distance between this vector and the remaining training vectors, and found the reference feature vector with minimum mean distance. (4) 2) Feature-Dis tance Vector Calculation: Given the reference feature vector for each subject, we then computed the feature-distance vector between a new mouse feature vector and the reference vector.Let be the reference feature vector for one subject; then for any new feature vector (either from the legitimate user or an impostor), we can compute the corresponding distance vector by (1), (2) and (3). In this paper, we used all mouse features in Table II to generate the feature-distance vector. There are 10 click-related features, 16 distance-related features, 16 time-related features, 16 speed-related features, and 16 acceleration-related features, which were taken together and then transformed to a 74-dimensional feature-distance vector that represents each mouse-operation sample. C.Eigenspace Computation: Training and Projection It is usually undesirable to use all components in the feature vector as input for the classi? er, because much of data will not provide a signi? cant degree of uniquene ss or consistency. We therefore applied an eigenspace-transformation technique to extract the principal components as classi? er input. 1) Kernel PCA Training: Kernel principal component analysis (KPCA) [37] is one approach to generalizing linear PCA to nonlinear cases using kernel methods. In this study, the purpose of KPCA is to obtain the principal components of the original feature-distance vectors.The calculation process is illustrated as follows: For each subject, the training set represents a set of feature-distance vectors drawn from her own data. Let be the th feature-distance vector in the training set, and be the number of such vectors. We ? rst mapped the measured vectors into the hyperdimensional feature space by the nonlinear mapping Here we centered the mapped point with the corresponding mean as . The principal components were then computed by solving the eigenvalue problem: (7) where and . Then, by de? ning a kernel matrix (8) we computed an eigenvalue problem for t he coef? ients is now solely dependent on the kernel function , that (9) For details, readers can refer to B. Scholkopf et al. [37]. Generally speaking, the ? rst few eigenvectors correspond to large eigenvalues and most information in the training samples. Therefore, for the sake of providing the principal components to represent mouse behavior in a low-dimensional eigenspace, and for memory ef? ciency, we ignored small eigenvalues and their corresponding eigenvectors, using a threshold value (10) is the accumulated variance of the ? st largest eigenwhere values with respect to all eigenvalues. In this study, was chosen as 0. 95 for all subjects, with a range from 0 to 1. Note that we used the same for different subjects, so may be different from one subject to another. Speci? cally, in our experiments, we observed that the number of principal components for different subjects varied from 12 to 20, and for an average level, 17 principal components are identi? ed under the threshold of 0. 95. 2) Kernel PCA Projection: For the selected subject, taking the largest eigenvalues and he associated eigenvectors, the transform matrix can be constructed to project an original feature-distance vector into a point in the -dimensional eigenspace: (11) As a result, each subject’s mouse behavior can be mapped into a manifold trajectory in such a parametric eigenspace. It is wellknown that is usually much smaller than the dimensionality of the original feature space. That is to say, eigenspace analysis can dramatically reduce the dimensionality of input samples. In this way, we used the extracted principal components of the feature-distance vectors as input for subsequent classi? ers. 22IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 V. CLASSIFIER IMPLEMENTATION This section explains the classi? er that we used, and introduces two other widely-used classi? ers. Each classi? er analyzes mouse-behavior data, and discriminates between a legitimate user and impostors. A. One-Class Classi? er Overview User authentication is still a challenging task from the pattern-classi? cation perspective. It is a two-class (legitimate user versus impostors) problem. In the scenario of mouse-dynamicsbased user authentication, a login user is required to provide the user name and to perform a speci? mouse-operation task which would be secret, like a password. Each user would choose her own mouse-operations task, and would not share that task with others. Thus, when building a model for a legitimate user, the only behavioral samples of her speci? c task are her own; other users’ (considered as impostors in our scenario) samples of this task are not readily available. In this scenario, therefore, an appropriate solution is to build a model based only on the legitimate user’s data samples, and use that model to detect impostors. This type of problem is known as one-class classi? ation [43] or novelty/anomaly detection [25], [26]. We thus focused our attention on this type of problem, especially because in a real-world situation we would not have impostor renditions of a legitimate user’s mouse operations anyway. B. Our Classi? er—One-Class Support Vector Machine Traditional one-class classi? cation methods are often unsatisfying, frequently missing some true positives and producing too many false positives. In this study, we used a one-class Support Vector Machine (SVM) classi? er, introduced by Scholkopf et al. [36], [38]. One-class SVMs have been successfully applied to a number of real-life classi? ation problems, e. g. , face authentication, signature veri? cation and keystroke authentication [1], [23]. In our context, given training samples belonging to one subject, , each sample has features (corresponding to the principal components of the feature-distance vector for that subject). The aim is to ? nd a hyperplane that separates the data points by the largest margin. To separ ate the data points from the origin, one needs to solve the following dual quadratic programming problem [36], [38]: the origin, and is the kernel function. We allow for nonlinear decision boundaries. Then the decision function 13) will be positive for the examples from the training set, where is the offset of the decision function. In essence, we viewed the user authentication problem as a one-class classi? cation problem. In the training phase, the learning task was to build a classi? er based on the legitimate subject’s feature samples. In the testing phase, the test feature sample was projected into the same high-dimensional space, and the output of the decision function was recorded. We used a radial basis function (RBF) in our evaluation, after comparative studies of linear, polynomial, and sigmoid kernels based on classi? ation accuracy. The SVM parameter and kernel parameter (using LibSVM [11]) were set to 0. 06 and 0. 004 respectively. The decision function would gen erate â€Å" † if the authorized user’s test set is input; otherwise it is a false rejection case. On the contrary, â€Å" † should be obtained if the impostors’ test set is the input; otherwise a false acceptance case occurs. C. Other Classi? ers—Nearest Neighbor and Neural Network In addition, we compared our classi? er with two other widely-used classi? ers, KNN and neural network [12]. For KNN, in the training phase, the nearest neighbor classi? r estimated the covariance matrix of the training feature samples, and saved each feature sample. In the testing phase, the nearest neighbor classi? er calculated Mahalanobis distance from the new feature sample to each of the samples in the training data. The average distance, from the new sample to the nearest feature samples from the training data, was used as the anomaly score. After multiple tests with ranging from 1 to 5, we obtained the best results with , detailed in Section VII. For the neural network, in the training phase a network was built with input nodes, one output node, and hidden nodes.The network weights were randomly initialized between 0 and 1. The classi? er was trained to produce a 1. 0 on the output node for every training feature sample. We trained for 1000 epochs using a learning rate of 0. 001. In the testing phase, the test sample was run through the network, and the output of the network was recorded. Denote to be the output of the network; intuitively, if is close to 1. 0, the test sample is similar to the training samples, and with close to 0. 0, it is dissimilar. VI. EVALUATION METHODOLOGY This section explains the evaluation methodology for mouse behavior analysis.First, we summarize the dataset collected in Section III. Next, we set up the training and testing procedure for our one-class classi? ers. Then, we show how classi? er performance was calculated. Finally, we introduce a statistical testing method to further analyze experimental results. (12) where is the vector of nonnegative Lagrangian multipliers to be determined, is a parameter that controls the trade-off between maximizing the number of data points contained by the hyperplane and the distance of the hyperplane from SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 23A. Dataset As discussed in Section III, samples of mouse-behavior data were collected when subjects performed the designed mouseoperation task in a tightly-controlled environment. All 37 subjects produced a total of 5550 mouse-operation samples. We then calculated feature-distance vectors, and extracted principal components from each vector as input for the classi? ers. B. Training and Testing Procedure Consider a scenario as mentioned in Section V-A. We started by designating one of our 37 subjects as the legitimate user, and the rest as impostors. We trained the classi? er and ested its ability to recognize the legitimate user and impostors as follows: Step 1: We trained the classi? er to b uild a pro? le of the legitimate user on a randomly-selected half of the samples (75 out of 150 samples) from that user. Step 2: We tested the ability of the classi? er to recognize the legitimate user by calculating anomaly scores for the remaining samples generated by the user. We designated the scores assigned to each sample as genuine scores. Step 3: We tested the ability of the classi? er to recognize impostors by calculating anomaly scores for all the samples generated by the impostors.We designated the scores assigned to each sample as impostor scores. This process was then repeated, designating each of the other subjects as the legitimate user in turn. In the training phase, 10-fold cross validation [24] was employed to choose parameters of the classi? ers. Since we used a random sampling method to divide the data into training and testing sets, and we wanted to account for the effect of this randomness, we repeated the above procedure 50 times, each time with independently selected samples drawn from the entire dataset. C. Calculating Classi? r Performance To convert these sets of classi? cation scores of the legitimate user and impostors into aggregate measures of classi? er performance, we computed the false-acceptance rate (FAR) and false-rejection rate (FRR), and used them to generate an ROC curve [42]. In our evaluation, for each user, the FAR is calculated as the ratio between the number of false acceptances and the number of test samples of impostors; the FRR is calculated as the ratio between the number of false rejections and the number of test samples of legitimate users.Then we computed the average FAR and FRR over all subjects. Whether or not a mouse-operation sample generates an alarm depends on the threshold for the anomaly scores. An anomaly score over the threshold indicates an impostor, while a score under the threshold indicates a legitimate user. In many cases, to make a user authentication scheme deployable in practice, minimizing the possibility of rejecting a true user (lower FRR) is sometimes more important than lowering the probability of accepting an impostor [46]. Thus we adjusted the threshold according to the FRR for the training data.Since calculation of the FRR requires only the legitimate user’s data, no impostor data was used for determining the threshold. Speci? cally, the threshold is set to be a variable ranging from , and will be chosen with a relatively low FRR using 10-fold cross validation on the training data. After multiple tests, we observe that setting the threshold to a value of 0. 1 yields a low FRR on average2. Thus, we show results with a threshold value of 0. 1 throughout this study. D. Statistical Analysis of the Results To evaluate the performance of our approach, we developed a statistical test using the half total error rate (HTER) and con? ence-interval (CI) evaluation [5]. The HTER test aims to statistically evaluate the performance for user authentication, which is de ? ned by combining false-acceptance rate (FAR) and falserejection rate (FRR): (14) Con? dence intervals are computed around the HTER as , and and are computed by [5]: (15) % % % (16) where NG is the total number of genuine scores, and NI is the total number of impostor scores. VII. EXPERIMENTAL RESULTS AND ANALYSIS Extensive experiments were carried out to verify the effectiveness of our approach. First, we performed the authentication task using our approach, and compared it with two widely-used classi? rs. Second, we examined our primary results concerning the effect of eigenspace transformation methods on classi? er performance. Third, we explored the effect of sample length on classi? er performance, to investigate the trade-off between security and usability. Two additional experiments are provided to compare our method with other approaches in the literature. A. Experiment 1: User Authentication In this section, we conducted a user authentication experiment, and compared our c lassi? er with two widely-used ones as mentioned in Section V-C. The data used in this experiment consisted of 5550 samples from 37 subjects.Fig. 3 and Table III show the ROC curves and average FARs and FRRs of the authentication experiment for each of three classi? ers, with standard deviations in parentheses. Table III also includes the average authentication time, which is the sum of the average time needed to collect the data and the average time needed to make the authentication decision (note that since the latter of these two times is always less than 0. 003 seconds in our classi? ers, we ignore it in this study). Our ? rst observation is that the best performance has a FAR of 8. 74% and a FRR of 7. 96%, obtained by our approach (one-class SVM).This result is promising and competitive, and the behavioral samples are captured over a much shorter period of time 2Note that for different classi? ers, there are different threshold intervals. For instance, the threshold interval fo r neural network detector is [0, 1], and for one. For uniform presentation, we mapped all of intervals class SVM, it is . to 24 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 TABLE IV HTER PERFORMANCE AND CONFIDENCE INTERVAL AT CONFIDENCE LEVELS DIFFERENT Fig. 3. ROC curves for the three different classi? rs used in this study: oneclass SVM, neural network, and nearest neighbor. TABLE III FARs AND FRRs OF USER AUTHENTICATION EXPERIMENT (WITH STANDARD DEVIATIONS IN PARENTHESES) information about mouse behavior, which could enhance performance. Finally, we conducted a statistical test, using the HTER and CI evaluation as mentioned in Section VI-D, to statistically evaluate the performance of our approach. Table IV summarizes the results of this statistical evaluation at different con? dence levels. The result shows that the proposed approach provides the lowest HTER in comparison with the other two classi? ers used in our study; the 95% con? ence interval lies at % %. B. Experiment 2: Effect of Eigenspace Transformation This experiment examined the effect of eigenspace-transformation methods on classi? er performance. The data used were the same as in Experiment 1. We applied a one-class SVM classi? er in three evaluations, with the inputs respectively set to be the original feature-distance vectors (without any transformations), the projection of feature-distance vectors by PCA, and the projection of feature-distance vectors by KPCA. Fig. 4 and Table V show the ROC curves and average FARs and FRRs for each of three feature spaces, with standard deviations in parentheses.As shown in Fig. 4 and Table V, the authentication accuracy for the feature space transformed by KPCA is the best, followed by the accuracies for feature spaces by PCA and the original one. Speci? cally, direct classi? cation in the original feature space (without transformations) produces a FAR of 15. 45% and FRR of 15. 98%. This result is not encouraging c ompared to results previously reported in the literature. However, as mentioned in Experiment 1, the samples may be subject to more behavioral variability compared with previous work, because previous work analyzed mouse behaviors over a longer period of observation.Moreover, we observe that the authentication results of % % by PCA, and % % by KPCA are much better than for direct classi? cation. This result is a demonstration of the effectiveness of the eigenspace transformation in dealing with variable behavior data. Furthermore, we ? nd that the performance of KPCA is slightly superior to that of PCA. This may be due to the nonlinear variability (or noise) existing in mouse behaviors, and KPCA can reduce this variability (or noise) by using kernel transformations [29].It is also of note that the standard deviations of FAR and FRR based on the feature space transformed by KPCA and PCA are smaller than those of the original feature space (without transformations), indicating that th e eigenspace-transformation technique enhances the stability and robustness of our approach. compared with previous work. It should be noted that our result does not yet meet the European standard for commercial biometric technology, which requires near-perfect accuracy of 0. 001% FAR and 1% FRR [10]. But it does demonstrate that mouse dynamics could provide valuable information in user authentication tasks.Moreover, with a series of incremental improvements and investigations (e. g. , outlier handling), it seems possible that mouse dynamics could be used as, at least, an auxiliary authentication technique, such as an enhancement for conventional password mechanisms. Our second observation is that our approach has substantially better performance than all other classi? ers considered in our study. This may be due to the fact that SVMs can convert the problem of classi? cation into quadratic optimization in the case of relative insuf? ciency of prior knowledge, and still maintain hig h accuracy and stability.In addition, the standard deviations of the FAR and FRR for our approach are much smaller than those for other classi? ers, indicating that our approach may be more robust to variable behavior data and different parameter selection procedures. Our third observation is that the average authentication time in our study is 11. 8 seconds, which is impressive and achieves an acceptable level of performance for a practical application. Some previous approaches may lead to low availability due to a relatively-long authentication time. However, an authentication time of 11. seconds in our study shows that we can perform mouse-dynamics analysis quickly enough to make it applicable to authentication for most login processes. We conjecture that the signi? cant decrease of authentication time is due to procedural features providing more detailed and ? ne-grained SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 25 TABLE VI FARs AND FRRs OF DIFFERENT SAMPLE LENGTH S Fig. 4. ROC curves for three different feature spaces: the original feature space, the projected feature space by PCA, and the projected feature space by KPCA.TABLE V FARs AND FARs FOR THREE DIFFERENT FEATURE SPACES (WITH STANDARD DEVIATIONS IN PARENTHESES) the needs of the European Standard for commercial biometric technology [10]. We ? nd that after observing 800 mouse operations, our approach can obtain a FAR of 0. 87% and a FRR of 0. 69%, which is very close to the European standard, but with a corresponding authentication time of about 10 minutes. This long authentication time may limit applicability in real systems. Thus, a trade-off must be made between security and user acceptability, and more nvestigations and improvements should be performed to secure a place for mouse dynamics in more pragmatic settings. D. Comparison User authentication through mouse dynamics has attracted growing interest in the research community. However, there is no shared dataset or baseline algor ithm for measuring and determining what factors affect performance. The unavailability of an accredited common dataset (such as the FERET database in face recognition [32]) and standard evaluation methodology has been a limitation in the development of mouse dynamics.Most researchers trained their models on different feature sets and datasets, but none of them made informed comparisons among different mouse feature sets and different results. Thus two additional experiments are offered here to compare our approach with those in the literature. 1) Comparison 1: Comparison With Traditional Features: As stated above, we constructed the feature space based on mouse clicks and mouse movements, consisting of holistic features and procedural features. To further examine the effectiveness of the features constructed in this study, we provide a comparative experiment. We chose the features used by Gamboa et al. 17], Aksari and Artuner [4], Hashia et al. [19], Bours and Fullu [8], and Ahmed a nd Traore [2], because they were among the most frequently cited, and they represented a relatively diverse set of mouse-dynamics features. We then used a one-class SVM classi? er to conduct the authentication experiment again on our same dataset with both the feature set de? ned in our study, and the feature sets used in other studies. Hence, the authentication accuracies of different feature sets can be compared. Fig. 5 and Table VII show the ROC curves and average FARs and FRRs for each of six feature sets, with standard deviations in parentheses.We can see that the average error rates for the feature set from our approach are much lower than those of the feature sets from the literature. We conjecture that this may be due to the procedural features providing ? ne-grained information about mouse behavior, but they may also be due, in part, to: (1) partial adoption of features de? ned in previous approaches C. Experiment 3: Effect of Sample Length This experiment explored the effe ct of sample length on classi? er performance, to investigate the trade-off between security (authentication accuracy) and usability (authentication time).In this study, the sample length corresponds to the number of mouse operations needed to form one data sample. Each original sample consists of 32 mouse operations. To explore the effect of sample length on the performance of our approach, we derived new datasets with different sample lengths by applying bootstrap sampling techniques [13] to the original dataset, to make derived datasets containing the same numbers of samples as the original dataset. The new data samples were generated in the form of multiple consecutive mouse samples from the original dataset. In this way, we considered classi? r performance as a function of the sample length using all bootstrap samples derived from the original dataset. We conducted the authentication experiment again (using one-class SVM) on six derived datasets, with and 800 operations. Table VI shows the FARs and FRRs at varying sample lengths, using a one-class SVM classi? er. The table also includes the authentication time in seconds. The FAR and FRR obtained using a sample length of 32 mouse operations are 8. 74% and 7. 96% respectively, with an authentication time of 11. 8 seconds. As the number of operations increases, the FAR and FRR drop to 6. 7% and 6. 68% for the a data sample comprised of 80 mouse operations, corresponding to an authentication time of 29. 88 seconds. Therefore, we may conclude that classi? er performance almost certainly gets better as the sample length increases. Note that 60 seconds may be an upper bound for authentication time, but the corresponding FAR of 4. 69% and FRR of 4. 46% are still not low enough to meet 26 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 1, JANUARY 2013 Fig. 5. ROC curves for six different feature sets: the feature set in our study, and the features sets in other studies.RESULTS OF TABLE VII CO MPARISON WITH SOME TRADITIONAL FEATURES (WITH STANDARD DEVIATIONS IN PARENTHESES) Note that this approach [2] is initially applied to intrusion detection, and we extracted parts of features closely related to mouse operations in our dataset. The reason for this decision is that we want to examine whether the features employed in intrusion detection can be used in user authentication. because of different data-collection environments; (2) using different types of thresholds on the anomaly scores; (3) using less enrollment data than was used in previous experiments.The improved performance based on using our features also indicates that our features may allow more accurate and detailed characterization of a user’s unique mouse behavior than was possible with previously used features. Another thing to note from Table VII is that the standard deviations of error rates for features in our study are smaller than those for traditional features, suggesting that our features might be more stable and robust to variability in behavior data. One may also wonder how much of the authentication accuracy of our approach is due to the use of procedural features or holistic features.We tested our method using procedural features and holistic features separately, and the set of procedural features was the choice that proved to perform better. Specifically, we observe that the authentication accuracy of % % by using the set of procedural features is much better than for the set of holistic features, which have a FAR of 19. 58% and a FRR of 17. 96%. In combination with the result when using all features, it appears that procedural features may be more stable and discriminative than holistic features, which suggests that the procedural features contribute more to the authentication accuracy.The results here only provide preliminary comparative results and should not be used to conclude that a certain set of mouse features is always better than others. Each feature set has it s own unique advantages and disadvantages under different conditions and applications, so further evaluations and comparisons on more realistic and challenging datasets are needed. 2) Comparison 2: Comparison With Previous Work: Most previous approaches have either resulted in poor performance (in terms of authentication accuracy or time), or have used data of limited size.In this section, we show a qualitative comparison of our experimental results and settings against results of previous work (listed in Table VIII). Revett et al. [34] and Aksari and Artuner [4] considered mouse dynamics as a standalone biometric, and obtained an authentication accuracy of ERR around 4% and 5. 9% respectively, with a relatively-short authentication time or small number of mouse operations. But their results were based on a small pool of users (6 users in [34] and 10 users in [4]), which may be insuf? ient to obtain a good, steady result. Our study relies on an improved user authentication methodolo gy and far more users, leading us to achieve a good and robust authentication performance. Ahmed and Traore [2] achieved a high authentication accuracy, but as we mentioned before, it might be dif? cult to use such a method for user authentication since the authentication time or the number of mouse operations needed to verify a user’s identity is too high to be practical for real systems. Additionally, Hashia et al. 19] and Bours and Fulla [8] could perform user authentication in a relatively-short time, but they reported unacceptably high error rates (EER of 15% in [19], and EER of 26. 8% in [8]). In our approach we can make an authentication decision with a reasonably short authentication time while maintaining high accuracy. We employ a one-class classi? er, which is more appropriate for mouse-dynamics-based user authentication. As mentioned in Experiment 3, we can make an authentication decision in less than 60 seconds, with corresponding error rates are FAR of 4. 9% and FRR of 4. 46%. Although this result could be improved, we believe that, at our current performance level, mouse dynamics suf? ce to be a practical auxiliary authentication mechanism. In summary, Comparison 1 shows that our proposed features outperform some traditional features used in previous studies, and may be more stable and robust to variable behavior data. Comparison 2 indicates that our approach is competitive with existing approaches in authentication time while maintaining high accuracy.More detailed statistical studies on larger and more realistic datasets are desirable for further evaluations. VIII. DISCUSSION AND EXTENSION FOR FUTURE WORK Based on the ? ndings from this study, we take away some messages, each of which may suggest a trajectory for future work. Additionally, our work highlights the need for shared data and resources. A. Success Factors of Our Approach The presented approach achieved a short authentication time and relatively-high accuracy for mouse-dynami cs-based user SHEN et al. : USER AUTHENTICATION THROUGH MOUSE DYNAMICS 27 TABLE VIII COMPARISON WITH PREVIOUS WORKAuthentication time was not explicitly reported in [4], [8], [17]; instead, they required the user to accomplish a number of mouse operations for each authentication (15 clicks and 15 movements for [17]; 10 clicks and 9 movements for [4]; 18 short movements without pauses for [8]). Authentication time was not explicitly stated in [2]; however, it can be assumed by data-collection progress. For example, it is stated in [2] that an average of 12 hours 55 minutes of data were captured from each subject, representing an average of 45 sessions. We therefore assume that average session length is 12. 5 60/45 17. 22 minutes 1033 seconds. authentication. However, it is quite hard to point out one or two things that may have made our results better than those of previous work, because (1) past work favored realism over experimental control, (2) evaluation methodologies were incons istent among previous work, and (3) there have been no public datasets on which to perform comparative evaluations. Experimental control, however, is likely to be responsible for much of our success. Most previous work does not reveal any particulars in controlling experiments, while our work is tightly controlled.We made every effort to control experimental confounding factors to prevent them from having unintended in? uence on the subject’s recorded mouse behavior. For example, the same desktop computer was used for data collection for all subjects, and all system parameters relating to the mouse were ? xed. In addition, every subject was provided with the same instructions. These settings suggest strongly that the differences in subjects were due to individually detectable mouse-behavior differences among subjects, and not to environmental variables or experimental conditions.We strongly advocate the control of potential confounding factors in future experiments. The reaso n is that controlled experiments are necessary to reveal causal connections among experimental factors and classi? er performance, while realistic but uncontrolled experiments may introduce confounding factors that could in? uence experimental outcomes, which would make it hard to tell whether the results of those evaluations actually re? ect detectable differences in mouse behavior among test subjects, or differences among computing environments.We had more subjects (37), more repetitions of the operation task (150), and more comprehensive mouse operations (2 types of mouse clicks, 8 movement directions, and 3 movement distance ranges) than most studies did. Larger subject pools, however, sometimes make things harder; when there are more subjects there is a higher possibility that two subjects will have similar mouse behaviors, resulting in more classi? cation errors. We proposed the use of procedural features, such as the movement speed curve and acceleration curve, to provide mor e ? egrained information about mouse behavior than some traditional features. This may allow one to accurately describe a user’s unique mouse behavior, thus leading to a performance improvement for mouse-dynamics-based user authentication. We adopted methods for distance measurement and eigenspace transformation for obtaining principal feature components to ef? ciently represent the original mouse feature space. These methods not only overcome within-class variability of mouse behavior, but also preserve between-class differences of mouse behavior. The improved authentication accuracies demonstrate the ef? acy of these methods. Finally, we used a one-class learning algorithm to perform the authentication task, which is more appropriate for mousedynamics-based user authentication in real applications. In general, until there is a comparative study that stabilizes these factors, it will be hard to be de? nitive about the precise elements that made this work successful. B. Oppor tunities for Improvement While previous studies showed promising results in mouse dynamics, none of them have been able to meet the requirement of the European standard for commercial biometric technology.In this work, we determined that mouse dynamics may achieve a pragmatically useful level of accuracy, but with an impractically long authentic

Character Development

Describe in detail the THREE most important things which happen to Amir which cause him to change his attitude to life. Find quotation and detail from the text to support your argument. I think that the three most important events in the book that effect Amir’s character is.. 1) When he watches Assef rape Hassan. This while it does not effect Amirs’s character in a positive way, still has a profound effect on him. He is wracked with guilt that haunts him though his life. It is a ‘metaphorical demon’ that he must face. After allowing Hassan to be raped, Amir is not any happier. On the contrary, his guilt is relentless, and he recognizes his selfishness cost him his happiness rather than increasing it. â€Å"That was a long time ago, but it’s wrong what they say about the past, I’ve learned, about how you can bury it. Because the past claws its way out. Looking back now, I realize I have been peeking into that deserted alley for the last twenty-six years. † To the reader, the quotation functions as a teaser. It piques the reader’s interest without revealing exactly what Amir is talking about, and from the time period Amir mentions, twenty-six years, the reader gets an idea of just how important this moment was. As the story unfolds, we realize that the deserted alley Amir refers to is where Hassan was raped, and that this event has largely defined the course of Amir’s life since. This is what Amir means when he says that the past continues to claw its way out. Try as he might to bury it, he was unable to because his feelings of guilt kept arising. As a result, he figuratively continues peeking into the alley where Assef raped Hassan, literally meaning that he keeps going over the event in his mind. â€Å"I actually aspired to cowardice, because the alternative, the real reason I was running, was that Assef was right: Nothing was free in this world. Maybe Hassan was the price I had to pay, the lamb I had to slay, to win Baba. † When Amir says this, toward the end of Chapter 7, he has just watched Assef rape Hassan,and rather than intervene, he ran away. Amir says he aspired to cowardice because, in his estimation, what he did was worse than cowardice. If fear of being hurt by Assef were the main reason he ran, Amir suggests that at least would have been more justified. Instead, he allowed the rape to happen because he wanted the blue kite, which he thought would prove to Baba that he was a winner like him, earning him Baba’s love and approval. The price of the kite, as Amir says, was Hassan, and this is why Amir calls Hassan the lamb he had to slay. He draws a comparison between Hassan and the lamb sacrificed during the Muslim holiday of Eid Al-Adha to commemorate Abraham’s near sacrifice of his son to God. In this context, Hassan was the sacrifice Amir had to make to get the kite and ultimately to gain Baba’s affection. 2) the discovery of Sohrab. Once Amir has married and established a career, only two things prevent his complete happiness: his guilt and his inability to have a child with Soraya. Sohrab, who acts as a substitute for Hassan to Amir, actually becomes a solution to both problems. Amir describes Sohrab as ‘looking like a sacrificial lamb’during his confrontation with Assef, but it is actually himself that Amir courageously sacrifices. In doing this, as Hassan once did for him, Amir redeems himself, which is why he feels relief even as Assef beats him. Amir also comes to see Sohrab as a substitute for the child he and Soraya cannot have, and as a self-sacrificing father figure to Sohrab, Amir assumes the roles of Baba and Hassan. 3)The confintation with Assef. My body was broken—just how badly I wouldn’t find out until later—but I felt healed. Healed at last. I laughed. † This quotation occurs during Amir’s meeting with Assef as he tries to find Sohrab in Chapter 22. Assef beats Amir with brass knuckles, snapping Amir’s ribs, splitting his lip and busting his jaw, and breaking the bone beneath his left eye, but because Amir feels he deserves this, he feels relief. He thinks he should have accepted the beating from Assef years ago, when he was given the choice of saving Hassan—and likely getting physically hurt—or letting Assef rape Hassan. Since that time, Amir has struggled with his guilt, which was only made worse by the fact that he was never punished for his actions. He had even gone looking for punishment in the past, as when he tried to get Hassan to hit him with the pomegranates, because he felt then there would at least be some justice for the way he treated Hassan. But Amir’s guilt lingered until his confrontation with Assef, which despite the physical pain, made him feel psychologically healed. Thus, while Assef beat him, he began to laugh. For each of these ‘life lessons’, describe how Amir changes /what he learns. (How is Amir different at the end of the novel)? 1)The rape, at first,does not have a positive outcome to Amir’s personality. But it was a cross road. The rape made Amir wake up to the true world. Because he chose not to help the guilt haunts him. This guilt is the metaphorical demon of Amir. If it wasn’t for this guilt Amir would have never found the motivation to help Sohrab. The book is about redemption, redemption of this guilt. But because he feels guilt about the rape, and how it has troubled him for the past 26 year shows how deeply he actually cared about Hassan. The raep motivates him though out the book to try and find a way to redeem himself to face down his demon. 2)The discovery of Sohrab gives Amir away to redeem himself for his past sins. He is to Amir â€Å"a sacrificial lamb† Because of this he becomes truly determined and dedicated to Sohrab. And the man he becomes through helping him is a man to be admired. 3)His confintation with Assef is to AMire a way to pay for his sins. Completely get ride of them. A way to ‘heal’ himself. This is where the man at the end of the book, a good man, comes into being. What do you think about Amir? Do you admire him or not? Explain why you feel like this? The central character of the story as well as its narrator, Amir has a privileged upbringing. His father, Baba, is rich by Afghan standards, and as a result, Amir grows up accustomed to having what he wants. The only thing he feels deprived of is a deep emotional connection with Baba, which he blames on himself. He thinks Baba wishes Amir were more like him, and that Baba holds him responsible for killing his mother, who died during his birth. Amir, consequently, behaves jealously toward anyone receiving Baba’s affection. His relationship with Hassan only exacerbates this. Though Hassan is Amir’s best friend, Amir feels that Hassan, a Hazara servant, is beneath him. When Hassan receives Baba’s attention, Amir tries to assert himself by passive-aggressively attacking Hassan. He mocks Hassan’s ignorance, for instance, or plays tricks on him. At the same time, Amir never learns to assert himself against anyone else because Hassan always defends him. All of these factors play into his cowardice in sacrificing Hassan, his only competition for Baba’s love, in order to get the blue kite, which he thinks will bring him Baba’s approval. The change in Amir’s character we see in the novel centers on his growth from a selfish child to a selfless adult. After allowing Hassan to be raped, Amir is not any happier. On the contrary, his guilt is relentless, and he recognizes his selfishness cost him his happiness rather than increasing it. Once Amir has married and established a career, only two things prevent his complete happiness: his guilt and his inability to have a child with Soraya. Sohrab, who acts as a substitute for Hassan to Amir, actually becomes a solution to both problems. Amir describes Sohrab as looking like a sacrificial lamb during his confrontation with Assef, but it is actually himself that Amir courageously sacrifices. In doing this, as Hassan once did for him, Amir redeems himself, which is why he feels relief even as Assef beats him. Amir also comes to see Sohrab as a substitute for the child he and Soraya cannot have, and as a self-sacrificing father figure to Sohrab, Amir assumes the roles of Baba and Hassan. So in conclusion I did not like Amir’s character at the bigining but I could understand it. On His path to redemption I admire him for just how much he did to achive his goal. His character at the end I truly admire,here is a man who made him self suffer for years and after reliving himself of that guilt is a truly better man. One that I am sure Baba would be proud of.

MD3 Assignment 3 Essay Example | Topics and Well Written Essays - 250 words

MD3 Assignment 3 - Essay Example What is the importance of education? Does education help individuals make decisions on what is good or wrong? At school, there are rules and regulations, students follow them and failure to do so, there are consequences. Do these rules help people to become successful in life, after attaining education? Imagine education as your brother, parent or even a friend. Would you want to let that person who you care for down? My guess is that every person works hard to have a good relationship with their loved ones so that they can get the best out of each other. The same way, education enables us to be better people or citizens in our country. Education is important since it creates a sense of goodwill among individuals, and this will ensure that peace prevails in the country if people get educated. Personal reference would be the most effective in giving a speech, since people remember most stories told of an individual’s personal life. This is because people can relate with the story from the past to what an individual has achieved in the present. It is also effective because it shows that the person giving the speech understands the topic perfectly through having personal experiences related to the

Information Systems-e-commerce and the Internet Essay

Information Systems-e-commerce and the Internet - Essay Example But it is only after the inevitable dotcom bust in the early years of the millennia that e-commerce established itself as a viable and dependable method of conducting business. Technological innovation in terms of developing security software aided this process and so did the process of globalization. As a culmination of these parallel but complementary processes, e-commerce in general and electronic financial transactions in particular has firmly taken root in mainstream global economy. Since the beginning of the 1990s, and with the advancement in global telecommunications technology, e-commerce has really taken off. And online retailing comprises an integral part of this broader phenomenon. In advanced countries, traditional brick and mortar retail stores have extended their services through the Internet, adding new dimensions to the experience of shopping for consumers. New and exclusive online retailers have also sprung up to cater to the newly created demand by consumers online. While there are complaints and concerns about this new mode of business (especially security issues), there are also numerous redeeming features of e-shopping, without which the market share for this fledgling industry would not have risen to 10 percent approximately.

Public Health Nursing Assignment Example | Topics and Well Written Essays - 250 words

Public Health Nursing - Assignment Example The important health status indicators include infant mortality rate, life expectancy at birth, maternal mortality ratio, neonatal mortality rate, under five mortality rate (child mortality rate). Good health indicator should show positive outcomes based on quantity and quality of people’s health status. Life expectancy at birth is the most vital health status indicator of any country which directly focuses on economic condition of the country. Life expectancy at birth refers to the mean number of years that a newborn baby can expect to live, if current mortality conditions are prevailed throughout the person’s life. This indicator focuses on age-specific probability of death of an individual by considering the present rate of death for people of varying ages in a country. Health status of a country is positively influenced by increase in life expectancy at birth. In economically sound countries like United States, life expectancy at birth is around 77 years. The countries having medium financial status like Jordan, life expectancy at birth is around 72 years. While in poor countries like Mali, life expectancy at birth is around 48 years. Thus, financial status of the country is directly proportional to the life expectancy at birth of that country. To address the major health concerns in poor countries various prevalent health problems should be attended to enhance the life expectancy at birth of the specific country. Thus, based on need of the country adequate and equal access to health care services could be delivered to enhance the life expectancy at birth. The enhanced life expectancy at birth could definitely achieve physical, mental and social well-being of the poor income country (Skolnik,

Religious Plurality as the Major Issue Surrounding Christianity in Research Paper

Religious Plurality as the Major Issue Surrounding Christianity in Africa - Research Paper Example I will try to cover different religions that exist in this continent. What are the major issues related to it? I will try to analyze them in the light of past and present so that a theme could be developed that connects the historical and contemporary world. In order to do so, we have to peep into the traditional religions of Africa as well as into Islam, this is predominantly the current religion of Africa. I will also try to analyze how religion affects the continent today and what are the defining factors. An analysis of the issues surrounding religion is not possible in isolation. A number of other factors have their own strong impact ( (Deagan) on it such as development, culture, democratic environment, health and security, conflicts, arms and reconstruction. Any study done without taking into consideration all these facts will not be based on reality. In short, we can say it will be inconclusive. So I will do a detailed analysis and will draw a conclusion based on all these factors. We cannot deny the social importance of religion. Religion is an important strand of society. It provides a value system that underpins the foundation of society. Without a common value system of which religion is an important thread, the concept of society is not possible. African history is very vast and old. Africa has a religious plurality. Religious plurality means sharing of different religious traditions by the same family, immediate or extended, similar ethnic groups and nations. Many religions exist side by side. Members of the same family may follow different religions. If we take a closer look we can categorize the religions in Africa into three tiers, Traditional religions, Christianity and Islam. After the Second World War self-concept of Africans has changed due to political, intellectual changes around them. The recorded history of Africa dates back to 3000 BC in Egypt. People worshipped gods like Isis,  Horus, Osiris, Ra, and Hapi. This was the case in Egypt. In West Africa, the trend was to worship the single godlike sky god.  

Gay Marriage Essay Example | Topics and Well Written Essays - 1000 words - 9

Gay Marriage - Essay Example Certain analysts state that psychological, physical and financial well-being is improved by marriage and that kids of gay couples benefit from being brought up by parents within a union that is legally recognized and is supported by institutions of the society. Court documents that American Scientist Associations filled also indicate that isolating gay women and men as ineligible for marriage both stigmatizes and welcomes discrimination by the public against them. The American Anthropological Association asserts that research of social science does not approve the opinion that either social orders that are viable or civilization depend upon failing to recognize gay marriage. Gay marriage can be carried out in a civil ceremony that is secular or in a religious setting. Many faith communities all over the world support are accepting gay couple to marry or perform gay marriage ceremonies. In a study of examining the consequences of discrimination that are institutional on the psychiatri c health of lesbian, gay, and bisexual (LGB) people that was carried out by a Columbia University Mailman School of Public Health, discovered an increase in psychiatric disorders, involving more than doubling of disorders of anxiety, within the (LGB) individuals living in the US that constituted bans on gay marriage. The study showed the importance of doing away with discriminations that are in the form of institutions, even those resulting in disparities in the well-being and mental health of LGB individuals.

Activity 4 Essay Example | Topics and Well Written Essays - 500 words

Activity 4 - Essay Example A contrary view may be correct to one individual, but completely incorrect to another individual based on actual case-by-case experiences. Rigidity of thinking likely exists within the disability field because of the particular philosophies that staff members and treating physicians have. Many professionals have a rigid belief about how to handle and properly care for these individuals, and some of the beliefs are based on theories. Instead of concentrating on the needs of the disabled persons, many people concentrate on the philosophy behind treatment and ignore what treatment will actually work. This type of â€Å"blindness† in the field must be eliminated in order to reach out and treat each individual disability and case as opposed to providing a generic â€Å"one size fits all† treatment method that may not be right for everyone. The Moore article described some of the problems that disabled individuals face when they re-enter into the real world. Many of these obstacles involve difficulty finding jobs, a lack of social opportunities and even having little or no control over how their care is arranged and provided. In other words, these individuals can be very powerless in the real world and a prejudice exists within society that attempts to restrict disabled individuals as an outer section so it does not affect the majority of people. These individuals are inappropriately shunned and do not receive the care they need to overcome their disabilities and become successful citizens and individuals. Regardless of staff or professional philosophies and beliefs, the ultimate goal should be to help these individuals to re-enter into society as functional individuals that are able to have control over their own

The Tunnel Rats Essay Example for Free

The Tunnel Rats Essay INTRODUCTION   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   It was in January 1966 when one of the biggest intelligence coups of the war that time took place. While the soldiers or the â€Å"diggers† were doing a sweep of the â€Å"Iron Triangle† that was an area near Saigon they discovered a vast complex of tunnels.[1] This location was heavily guarded with armed protection and was restricted by the Viet Cong (VC). What they were about to find out was 60 feet into the ground of that perimeter would be the Viet Cong headquarters. [2]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   When they arrived in Vietnam as part of the 1st Battalion of the 28th Infantry, 3rd Brigade of the 1st Infantry Division in January 7, 1966, they were called the â€Å"Big Red One† and were sent to engage in operation â€Å"Crimp.†[3] The mission was to search and destroy sweep the Viet Cong stationed in the Northwest of Saigon. Even as they were just landing, they could see from the air how there were groups of their men in trouble with small fire fights that made them quickly exit their helicopters to engage in battle and destroy the VC that had been attacking the soldiers.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   When they went inside the tree line that led to the locations were they saw some of the fights, they saw a large trench filled with nothing and no one. They did not know where the VCs went. Those they saw that were firing at the other soldiers just disappeared into thin air. They were gone, all of them.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The battalion moved forward to see large catches of rice and estimated the amount they saw were enough to feed a Regiment.   Even after a few days later, they saw foxholes, trenches and caves but there were no VC enemies to fight with. However, it was evident the United States casualties were still increasing because of sustained enemy sniper fire that basically came out of nowhere.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   It was already January 10th and they merely had a few glimpse of this enemy. Later that day, a radio report came out that elements from another brigade had made contact with the VC and found the same thing tunnels. VIETNAM WARFARE STATEGIES   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Later they would discover that the VC’s strategy was to strike unexpectedly and then slip away into the tunnels to avoid retaliation. Their strategy was concealment and was effective with their hit and run tactics.[4] Tunneling was the essential element in the VC strategy. It was the greatest element in the VC stationed in the area of Cu Chi, located north the South Vietnamese capital, Ho Chi Minh City (formerly known as Saigon).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   They used the tunnels for many functions. They attack American installations that were conveniently built right above them.[5] They took refuge in the tunnels when they felt threatened with annihilation. They used the tunnels to escape from threatened villages. The also used them to store war materials and to operate facilities like an underground hospital. The Americans never really discovered the full extent of the Viet Cong tunnel systems, but they gradually tried their best to develop tactics to counter attack the VCs and to use the complexities of the tunnels to their advantage.[6]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The tunnels are now made into something like a Disney amusement park wherein tourists can take an hour bus ride from Ho Chi Minh to experience going into them. These tunnels used to be well hidden from American soldiers and reporters of the war but now it has brought tourism to the country. But along with the sights and the interesting experience, the war tactics and weapons the Viet Cong used were placed on display that serves as a reminded of their polished warfare strategies.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Man traps made to kill were part of the display. When American soldiers would try to slip into the tunnels, Punjisticks or spears of bamboo with razor sharpness and covered with excrement or poison were the first things that greeted them.[7] The sticks pierced the legs and the torsos of the Americans. More brutal strategies were used as bear traps were also placed in the tunnel openings to amputate the feet of the soldiers as they go inside the tunnel. Like this was not enough protection, booby traps were also hung from the trees near the opening that would result to beheaded soldiers or amputated limbs.[8]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The VC did not even bury some of their casualties. The United States forces often buried their dead enemy to keep track of the extent of the casualties for the enemy. Tactical conditions from the Viet Congs would result to dead VCs lying around the tunnels for the tunnel rats to discover. In some cases, they would even pull the bodies of dead American soldiers in the tunnels for the soldiers to encounter. This was a strategy they used to unnerve and demoralize the U.S. troops that would come into the tunnels.[9] TUNNELS OF CU CHI   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Located just seventy-five kilometers northwest from the country’s capital Ho Chi Minh were the tunnels of Cu Chi. This district of Cu Chi was a major Viet Cong infiltration route that served as a trail towards the Ho Chi Minh.[10]   Situated above the ground of this perimeter was the station of the United States 25th Infantry Division.[11] Below them was the home of the 7th Viet Cong Regiment and other allied units. It was like sleeping with the enemy, only with them they were sleeping above the enemy, literally.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The tunnels were reported to be 250 kilometers long and most of the tunnels were located in Cu Chi. There were three levels to the VC tunnels. [12] In the first level, the opening was three meters deep. As one goes deeper into the tunnel, it would widen and be about six meters deep. The third level of the tunnels was eight meters below the ground. An Underground Battle Station   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   In the tunnels, it was like a whole word underground. There were kitchens, there was a hospital, and the officers’ quarters were there as well as a meeting room.[13] During that time the tunnels were almost inaccessible. It was hidden in a jungle-like area. During the war it was ravaged by a skin-burning chemical Agent Orange that was part of the American counterattack. Some tunnels were also too narrow for an overweight Westerner.[14]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The tunnel system included different sizes of chambers, rest areas, weapons and ammunitions storage, kitchens, workshops, barracks room as well as rooms that housed the communication equipments.[15]   The kitchens was designed so well the smoke that could signal the Americans of their location were dispersed and dissipated into numerous pipes that would mislead anyone who would see it.[16]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The tunnels of Cu Chi were the venue where the Viet Cong fighters and the American Tunnel Rats, as they call those brave enough to penetrate the tunnels, would go into hand-to-hand combat inside dark and dangerous subterranean and complex tunnels. These tunnels were where they fought to death using knives and pistols.[17] The Little IRT   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The tunnels of Cu Chi were known as the Little IRT. They were similar to the railway system in America with an interconnecting tunnel system that was in the northern section of Hau Nghia Province and in the southwester section of Binh Duong Province. The tunnels complexity connected hamlets, villages and provinces in the area. Originally it was dug up to be used to support the Viet Minh guerilla war against the French.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The American soldiers dubbed the tunnels the â€Å"little IRT† because of how similar it was to the New York City Subway. The complex tunnel system allowed for different levels in different locations that were interconnected by a series of â€Å"trap doors, channels, shafts, wells and communication tunnels.†[18] There connections from the tunnels to bunkers that was almost bombproof as well as to ground level bunkers. Tunnel Explorer, Locator Communicator System   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The TELACS was an experimental communications device system that the tunnel rats or the American soldiers used when they explored the enemy tunnel systems. It was a system that was composed of an earphone and a throat microphone for communications with the troops in the surface.[19]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   It was a flawed system because there was much voice distortion and there needed to be a large amount of wire that had to be dragged behind the explorer. It was a slow and inaccurate system. When it was tested in the year 1969, it was withdrawn.[20]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The tunnel system proved to a sophisticated military tactic from the Viet Cong that may have been underestimated by the American troops. The genius of their strategies overwhelmed U.S. Forces until it came to the point that they decided to clear the tunnels of the VC. TUNNEL CLEARING   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   After the time when Ben Suc, Vietnam was depopulated American troops went on to clear out the tunnels of Cu Chi, looking for Viet Cong fighters. The army made use of large tanks with bull dozer blades as well as medium built soldiers that were known to be â€Å"tunnel rats† that went to uncover the underground city.[21] In the clearing process they found stoves, furniture, clothes for men and women, and essentially thousands of pages of important war documents. This major headquarters that the American command found brought them to explore further into the tunnels.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   However, during that time, a senior officer that was in-charge of exploring the tunnel was killed by one of the bloody booby traps. The U.S. army saw the danger of the situation and retreated from exploring the tunnels. Instead, they pumped tear gas into the tunnels as well as set off explosives.[22] The Americans thought this was the top headquarters for the Viet Cong, they miss the headquarters of the NLF that was several miles north that place.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The U.S. used tons of artillery and bombs for every Viet Cong fighter. The Viet Cong manual even said that the U.S. had much superior weapons and strength compared to them on the battlefield. But they could not chase them as they always launched surprise attacks from their underground tunnels.[23]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The use of incendiary weapons that included the white phosphorous and the napalm was used vastly by the U.S. forces during this time. [24]This move has placed them in the center of condemnation.   Napalm was described to be a petroleum fuel that as very effective in the destruction of the enemies’ bunkers as well as the people inside them. White phosphorous was used to mark targets and to set fire to flammable ones. It has caused suffering that would tend to continue burning the skin long after the initial contact.[25] Used together with napalm would prove to be painfully lethal. The mortality rate from those who suffered from such weapons was high and there were deaths that arose from injuries where victims were too badly burnt to receive hospital treatment.[26] CS Gas was proficiently used in clearing the tunnel complexes that sifted the enemy soldiers as well as the large numbers of civilians who sought refuge in the tunnels.[27]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Some antiwar critics of the U.S. Forces in the Vietnam claimed that America conducted a war of genocide in Vietnam because of the civilian casualties. However this was denied because the U.S. military strategy did not amount to having an official policy of genocide nor was it the intention of the government and the armed forces to wipe out any significant part of the Vietnamese civilians. TUNNEL RATS   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   During the war trained special units were called tunnel rats. They were described to be â€Å"small, mean and crazy† as they actually went inside the tunnels and combated with Viet Congs they encountered while other units merely thrown explosives to clear the tunnels.[28] They were also known to be the â€Å"Tunnel Runners† by the 25th Infantry Division and â€Å"Ferrets† by the Australian Army. â€Å"Tunnel Rat† was their official accepted name.[29]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   It was during this time that the U.S. Army realized that it was short-sighted to destroy the tunnels by the bulldozers and the bombings. There would be a massive loss of vital intelligence if the plans and documents of the Viet Cong would be destroyed through their first strategy.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   It was in 1965 when the 1st and 25th Infantry division organized specialized teams that had missions to search and explore the tunnels in the III Corp area.[30] The tunnel rats were not assigned; they were all volunteers and were armed only with a pistol or shotgun, a knife and a flashlight.[31] They infiltrated the tunnels with such minimal weapons where hundreds of VC might be hiding with their massive supply of weapons stored in the tunnels as well. Anyone who went into the tunnels was then dubbed as â€Å"Tunnel Exploration Personnels.†   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   As the tunnel rats descended into the tunnels they experience walking into a pitch black and claustrophobic pathway wherein they were playing a deadly game of hide and seek with the enemy Viet Cong. The sensitive probing of the floor, sides and roofs of the tunnels soon became second nature to the tunnel rat as he inched his way deeper into the tunnel complexes.[32] They carefully watched out for wires and tree roots that was irregular and could pass for booby trap that could blow them up to pieces or cut their limbs into pieces.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The entrances of the tunnels alone are usually mined or protected by concealed guards ready to fire upon entry. Sometimes, the unsuspecting tunnel rat can met the garrote or someone would cut his throat as he came up and pass by connecting trapdoors[33].   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Aside from the VC booby traps were a whole breed of animals that resided in the dark confines of the tunnels.[34] There were bats that used the grounds during the daylight hours. Snakes were also encountered inside the tunnels. The Bamboo Viper and the Krait were the deadly snakes that can be found the VC tunnels. The Viet Cong would deliberately tether a snake in the tunnels to serve as a natural booby trap for the tunnel rats.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The stress the tunnel rats undergo every time they went into the tunnel was unthinkable that pushed their mental state to its limits. They would crawl into the narrow, pitch black tunnels looking for a heavily armed enemy for hours to combat with. The idea was to find the VC first before he jumped on them to kill them. Sometimes the strain on the men’s nerves was too much to bear to the point wherein he had to be dragged from the tunnel screaming and crying.[35] When this occurs, they are not allowed to go into the tunnel ever again. There were no dead tunnel rats that were to be left inside the tunnel. Dead or wounded they were dragged out with wires, rope or by a comrade only to be taken out of the VC territory.[36] Weapons and Warfare   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   There was extensive use of the tunnel by the VC. The tunnel rats had to search and flush out the VC. Tunnel warfare then occurred between the VC and the U.S. Tunnel Rats.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The soldiers became used to tunnel welfare that they reveled in the opportunity to pursue a VC through the narrow passageways. It was not a work for someone with a faint heart as the danger of death was ever present underground were grenades would just pop through trapdoors and other forms of booby trap awaited.[37]   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   When a tunnel rat went in for tunnel warfare, the infantry basic load was kept to a minimal. His total lack of equipment to carry was a factor for the tunnel rat’s survival.[38] The pistols the tunnel rats carried where the .38 Smith and Wesson. Sometimes they would carry a 9mm German Luger.[39] Most of the tunnel rats agreed not to carry the Colt .45. It was too big of a weapon for the underground battle with a silencer. Without a silencer, it was too loud that the enemies from far away could know your location instantly while you are temporarily deafened by the shot. In tunnel warfare, the tunnel rats follow the golden rule that prohibits firing more than three shots underground without reloading.[40] If this does not happen, the VC could know that the tunnel rat is out of ammunition and could attack while they reload.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The tunnel rats carried a standard Army issue flashlight and each member carried one. They practiced how they would carry the flashlight to prevent themselves from being lighted targets. They also practiced how to change batteries in pitch darkness by touch alone and how it can be done quickly.[41] CONCLUSION   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The tunnel rats were remembered to be one of the bravest in the American-Vietnam war. They did a job that not many wanted to do. In fact, not many dared to volunteer for this position. But they stepped up and made it a duty to their country.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   It was not an easy job considering the highly sophisticated tunnel strategies that the Viet Cong implemented before the U.S. Army troops even discovered that they existed. They were in and out of a battle scene. They controlled the tempo of the battle because of their invisibility. Even when the tunnels were discovered, threats still turned on the American troops as it proved to be dangerous to explore the tunnels. Deadly booby traps such as land mines, sharp sticks and trapdoors overwhelmed the U.S. troops that may have underestimated the intelligence of the VC tactics. In the end much of the tunnels that were deeper into the complexes were remained uncovered and unexplored by the U.S. Army. This was a war rightfully won by the one who had the best strategies, the most ruthless approaches that surprised the nations of the world. BIBLIOGRAPHY Brown, Lousie. War and Aftermath in Vietnam. New York, Routledge, 1991. Clark, Gregory R. Words of the Vietnam War: The Slang, Jargon Abbreviations, Acronyms Nomenclature, Nicknames Pseudonyms, Slogans Specs, Euphemisms Double-Talk, Chants and Names and Places of the Era of United States Involvement in Vietnam. Jefferson, NC, McFarland, 1990. Mangolds, Tom. â€Å"Behind Enemy Lines a Nam Vet Returns; Tom Mangold Revisits the Terrifying Viet Cong Tunnels He Discovered as a Young War Reporter.And Finds Them Transformed into a Fascinating, Disney-Style Attraction†, The Mail on Sunday, 15 October 2006, 94. McGibbon, Ian. â€Å"The Tunnels of Cu Chi: A Remarkable Story of War in Vietnam†, New Zealand Internationa lReview, Vol. 31, No. 3 (2006): 29. Philbert, Robert E.   â€Å"Back to Nam†, Social Studies, Vol. 86, No. 1 (1995): 6. Schulzinger, Robert D. A Time for War: The United States and Vietnam, 1941-1975. New York Oxford University Press, 1997. â€Å"Tunnel Rats.† Digger History, (2002). Available from http://www.diggerhistory.info/pages-conflicts-periods/vietnam/tunnel-rats.htm, accessed on October 3, 2007.