Proceedngs of Student-Faculty Research Day, CSIS, Pace Unversty, May nd, 014 Mouse Bometrc Authentcaton Francsco Betances, Adam Pne, Gerald Thompson, Hedeh Zandkarm, and Vnne Monaco Sedenberg School of CSIS, Pace Unversty, Whte Plans, New York Abstract Increased securty concerns wthn the computng world have forced securty-mnded users and developers to push for greater bometrc verfcaton technques. The use of a mouse as a bometrc verfcaton devce through the dentfcaton of unque user movements has ganed tracton and support wthn the ndustry. Although there s nterest n the subject, there s queston to the vablty of such a concept and ts ablty to wthstand unauthorzed access attempts by malcous users. Studes wthn the ndustry have shown that whle vablty exsts, mplementaton for end-user customers may be more dffcult than ntally planned. Index Terms Authentcaton, Mouse Bometrcs, Mouse Clcks, Mouse Velocty 1) INTRODUCTION development of advanced computng systems and Tthe transton of major ndustral, commercal, and consumer level tasks to a purely computng envronment has created a great concern n terms of computer securty. Whle passwords and tradtonal methods of computer securty are effectve, they are unable to verfy the bometrc dentty of the user; ths creates a weakness that can be exploted through varous access attempts to unverfable access tokens. The dea of usng a mouse or touchpad devce as a means of user authentcaton has potental as a method of bometrc securty, as such nputs would be dffcult to mtate by other ndvduals. Ths s based on the premse that ndvdual mouse movements are unque to the ndvdual, as no two users engage wth computer nterfaces n the same manner. To support ths research, a number of studes have shown promsng development through the dentfcaton of unque user patterns toward computer behavor. As such, mouse dynamcs serve as a potental behavoral dynamc that would be dffcult to mtate wthout drect knowledge of user actvtes. Mouse dynamcs has ts orgns n the concept of keyboard dynamcs, n whch the dentfcaton of userspecfc typng rhythms s used as a means of verfyng user dentty. From ths perspectve, mouse dynamcs are a natural next-step n the process and can be used n combnaton wth other behavoral bometrc methods to verfy user dentty. Whle keyboard dynamcs has been studed for several decades, the use of mouse dynamcs n ths concept s a recent phenomenon and remans largely untested, as t has not faced a smlar type of scrutny from the securty communty. However, several studes have reaffrmed that there s a hgh success rate n dentfyng users wth a very low rate of false postves and false negatves [1]. Although successful, there are varous methodologes for the mplementaton of the bometrc readngs wth degrees of accuracy. Most mportant, the need for bometrc authentcaton through behavoral readngs must be able to dentfy users whle preventng mtators from ganng successful entry nto the system. Because of the lmtatons of mouse dynamcs and authentcaton, t has been argued that ths form of bometrc dentfcaton s best equpped n a form that complements a prmary method, rather than relyng on ths type of method for the prmary dentfcaton method. ) MOUSE AND BIOMETRICS BACKGROUND a. Mouse Devce Background A mouse s synonymous wth computer use but not many people know exactly how t works. A mouse s a pontng devce that functons by detectng twodmensonal moton relatve to ts supportng surface []. The mouse devce s usually made up of two buttons and a scroll wheel, whch can also work as a thrd button. The left button s used for selectng tems for draggng and droppng, and t s also used for pressng buttons. By clckng the rght button, a user can access edtng propertes that the fle, webpage or applcaton may have. For mouse devces that have a scroll wheel, ths can be used for scrollng up or down on a webpage, an applcaton or a folder. The scroll wheel button also can be pressed and n certan stuatons t wll automatcally scroll the page n the drecton that mouse s movng, and ths stops by clckng the scroll wheel agan. In certan games, t can be mapped to do another functon and can be used lke a regular mouse button or keyboard button for accessng tems or certan objects n the game. b. Bometrcs Background Bometrcs s a way to dentfy people based on a partcular dstnctveness or a certan pattern. These patterns or dstnctveness can be a physcal trat or another B5.1
notceable characterstc that can be used to dentfy the user. There are certan types of characterstcs that are used to dentfy a person, ths ncludes ther fngerprnt, retnal scan, DNA, and facal or voce recognton. Based on the fact that bometrcs s based on a partcular dentfable trat, t s a good way to dentfy a person and as far as computer securty goes, be able to grant access to that person. There s usually some type of sensor that wll get the nformaton based on the gven trat and check t aganst a database of what the characterstc should be. For physcal trats, a camera can be used as a sensor because physcal characterstcs can be notceable. For bometrcs through computers, an applcaton can be used that ether logs keyboard strokes or mouse movements. Once the data s gathered, the acqured data s checked aganst the database to determne f the bometrc data, n fact, matches the ndvdual. data, the quantty of nput data s often used rather than tme..1 Related Research a. Identfyng Game Players Unversty of Washngton The Unversty of Washngton worked on dentfyng game players usng mouse movement n two popular vdeo games, Soltare, and StarCraft [4]. The process of collectng data s to perform the baselne experments, playng Soltare, and playng StarCraft. The players were all female and the experment ran on the same computer to ensure that all the parameters were consstent for all the users; n contrast to our data collecton we gather data usng dfferent computers. In the baselne experment, an applcaton was developed to gather data n a controlled envronment for each ndvdual; ths applcaton could capture three major mouse actons: mouse moves, clcks, and drags. The frst task requres users to clck rapdly and accurately between two targets, the second task requres the users to drag a crcular shape n a specfc range, and the fnal task requres users to double clck on a target. In the Soltare and StarCraft experment all the users play the game ndvdually and then the data s collected. Fg. 1. Ths mage shows the steps necessary for bometrc authentcaton [3]. c. False Acceptance and Rejecton Rate When dong a study on behavoral bometrcs, and user verfcaton usng a mouse, t s mperatve to study peers research on the topc. Ths has recently become a topc of great nterest, and t s mportant to examne current research. Many good deas have been developed on ths topc, but there are stll mprovements to be made. When lookng at two related research papers, t s not clear what the most effectve method would be to test for a proper valdaton system. Bometrc systems are typcally evaluated wth the followng varables [6]: False Acceptance Rate (FAR) - the probablty that the system wll ncorrectly label the actve user as the same user that produced the enrollment sgnature. False Rejecton Rate (FRR) - the probablty that the system wll ncorrectly label the actve user as an mpostor, when n fact t s not. Equal Error Rate (EER) - the error rate when the system's parameters (such as the decson threshold) are set such that the FRR and FAR are equal. A lower EER ndcates a more accurate system. Verfcaton tme- the tme requred by the system to collect suffcent behavoral data to make an authentcaton decson. Because there can be sgnfcant pauses n the Fg..Baselne program, clckng tasks, draggng tasks, and double clckng tasks [4]. B5.
In ther experment, they developed a C# program to log low-level mouse movements. Fg. 3. Vsualzaton of the frst three movement n a Soltare game. Frst column: locaton of a mouse event comprsng the acton. Second column: normalzed velocty for each mouse event [4]. After gatherng the data from the players, they used SVM, 1-Nearest Neghbor, and 7-Nearest Neghbor models. The Neghbor s model work more accurately than the SVM model accordng to ther experment. The models they constructed do not perform well across a game doman. However, t s accurate enough to dentfy cheatng players or unauthorzed users. b. Authentcaton Methods San Jose State Unversty San Jose State Unversty worked on mouse movement as a bometrc. They proposed two authentcaton methods, one for ntal logn of users and another for securty purposes to montor a computer for suspcous usage patterns, bascally ther authentcaton models works n two phases: enrollment and verfcaton [5]. The user enrolls n the system by movng the mouse to follow a sequence of dots presented on the screen. Durng the verfcaton phase, the user tres to logn by movng the mouse on the same pattern of dots as were presented durng the regstraton phase. The purpose of these experments s to calculate the error rate of ther authentcaton scheme and compare wth other bometrc research. In the enrollment phase the user logs nto the system, and they re supposed to move the mouse towards the dot that appears on the screen, clck on t, and the dot wll dsappear. Ths process has to be repeated ten tmes. Based on the user s mouse movements, the coordnates of the mouse are recorded. Speed, devaton from the straght lne and angles are calculated. The data collected n ths phase s beng used n the verfcaton phase when the user tres to log n. In the verfcaton phase, the system checks to see f the user s credentals are correct based on the data collected n the enrollment phase. To log n, the user follows the same pattern as the enrollment phase. In ther scheme, t takes 0 seconds for the user to complete logn verfcaton. The result from ths phase wll be compared wth the result calculated durng the regstraton. Ths model had been tested on 15 users all usng the same computer to ensure all the parameters that affect the accuracy of the system could reman constant. Ther system computed the error rate, n ther case the error-rate was 0%. Eventually ther goal s to have a system that works on a broad range of devces wth less false acceptance rate as well as false rejecton rate. c. Other Valdaton Systems Recent research nvestgated the possblty of determnng whether the user was an mposter or not [7]. When determnng verfcaton accuracy, one must look at the FRR and the FAR. FRR, or the false rejecton rate, s the probablty that the user s wrongly dentfed as an mposter. The FAR, or false acceptance rate, s when the mposter s ncorrectly dentfed as the user. In ths research, t was found that there was a FAR 1.53. Ths s B5.3
too hgh of a false acceptance rate, the European Standard for Access Control Systems requres under a.001% false acceptance rate. The FRR was recorded at 5.65, agan, the European Standard for Access Control Systems requres under a 1% false rejecton rate. Partcpants entered a nne dgt numbered code. The partcpants would enter ths code n wth a mouse, nto a, 0 9, keypad. Speed n a drecton, and dstance traveled were recorded. Ths study was done usng ten undergraduate students, ages -5. Possble ssues can arse wth such a small sample sze. It s not a good ndcaton of the general populaton. Another problem wth ths study s the testng method. The task chosen appears very smple. A task that s too smple could lead to a hgh FAR, whch makes the verfcaton method nsecure. It seems hghly lkely that an mposter may have very smlar behavoral movements for such short tmes and dstances; also t would seem to be easy to mmc another user s patterns n such a task. One addtonal test method for ths task could provde an mprovement. Perhaps f the angle n whch the user moves the mouse was also tested, t would decrease the FAR and FRR. It would add a unque behavor to the task, makng t more dffcult for an mposter to mmc. In a study by Zheng, Palosk, and Wang, research was done on how the mouse was moved versus where the mouse was moved [8]. User sessons were recorded, and the users mouse data were recorded. Ther study looked at the dfferent angles and trajectores the users had durng the user s sesson. The average FRR n ths study was.86% ant the average FAR was.96%.whle these numbers are much better than Sngh s study, t stll does not meet the requrements for the European Standard for Access Control Systems. 3) MEASURING MOUSE BIOMETRICS Mouse trajectores can arse from the followng actons: a. System wake up the mouse s moved or jggled to wake up the operatng system (no mouse clcks at ether end of the ) [9]. b. Move and clck the mouse s moved to a locaton on the screen to perform an acton such as clckng on an object, etc. The begns wthout a mouse event and ends n a mouse clck. c. Hghlght a secton of text or an object s hghlghted. Ths acton begns wth a left mouse clck/hold to begn the hghlghtng and ends wth the mouse release. d. Drag and drop an object s dragged and dropped. Ths acton begns wth a left mouse clck/hold and ends wth the mouse release. The above categores combned wth sesson level mouse trajectores produce 45 features shown n Table 1. Acton System wake up: The mouse s moved or gggled to wake up the operatng system (no mouse clcks at ether end of the ) Move and clck: The mouse s moved to a locaton on the screen to perform an acton such as clckng on an object, etc. The begns wthout a mouse event and ends n a mouse clck. Hghlght: A secton of text or an object s hghlghted. Ths acton begns wth a left mouse clck/hold to begn the hghlghtng and ends wth the mouse release. Basc Feature Measurements 1. From the number of ponts. From the tme of the 3. From the pont-to-pont dstance 4. From the length of the 5. From the pont-to-pont veloctes 6. From the pont-to-pont acceleratons 7. From the pont-to-pont drecton angle changes 8. From the number of nflecton ponts 9. From the curvness of the 1. From the number of ponts. From the tme of the 3. From the pont-to-pont dstance 4. From the length of the 5. From the pont-to-pont veloctes 6. From the pont-to-pont acceleratons 7. From the pont-to-pont drecton angle changes 8. From the number of nflecton ponts 9. From the curvness of the 1. From the number of ponts. From the tme of the 3. From the pont-to-pont dstance 4. From the length of the 5. From the pont-to-pont veloctes 6. From the pont-to-pont acceleratons 7. From the pont-to-pont drecton angle changes 8. From the number of nflecton ponts 9. From the curvness of the Sample For Each Feature mean (average), medan, mnmum, maxmum, standard devaton mean (average), medan, mnmum, maxmum, standard devaton mean (average), medan, mnmum, maxmum, standard devaton B5.4
Drag and drop: An object s dragged and dropped. Ths acton begns wth a left mouse clck/hold and ends wth the mouse release. 1. From the number of ponts. From the tme of the 3. From the pont-to-pont dstance 4. From the length of the 5. From the pont-to-pont veloctes 6. From the pont-to-pont acceleratons 7. From the pont-to-pont drecton angle changes 8. From the number of nflecton ponts 9. From the curvness of the Table 1: Sesson-Level Mouse Trajectory Features mean (average), medan, mnmum, maxmum, standard devaton There are dfferent formulas that can help n acqurng the data necessary to authentcate a user usng mouse bometrcs. The formulas below are used to get the wanted nformaton. a) Amount of ponts n ( p ) = 1 The number of ponts consttutes the entre ; ths formula determnes the number of ponts nvolved n the entre [9]. b) Amount of tme to complete n ( t - t 1) = Ths formula measures the amount of tme that the took to fnsh from the begnnng untl t reached the last pont. c) Length of the n ( x x 1 ) + ( y y 1) = Ths determnes the length of the by addng all of the pont to pont dstances, whch s determned by the formula nsde the square root. d) Velocty from pont to pont n the ( x x 1 ) + ( y y 1) V = t t 1 Ths formula takes nto account the length of the from one pont to another dvded by the tme that t took to go from the prevous pont to the current pont, so t calculates how rapdly t moved, whch s the velocty from pont to pont. a e) Acceleraton from pont to pont n the = V V 1 ( t t )/ 1 The velocty of the two ponts s dvded by the length of tme that t took to get from one pont to the next gets the acceleraton between the two ponts. f) Drecton angle from pont to pont The formula to get the angle s m = y x y x 1 1 Then, to get the change n angle between the two ponts, the formula s m m 1 3.1Mouse Clck Characterstcs A mouse clck takes place when a button of the mouse or the scroll wheel n the mddle s pressed and then released [9]. The fve types of mouse clck events: left clck, rght clck, double clck, hghlght, and drag and drop. The hghlght and drag-and-drop events are combned and are referred here as drag-and-drop events. Table dsplays the 9 features that were mplemented. Mouse Clck Event Rato of left clcks Rato of rght clcks Rato of double clcks Rato of drag-and-drop clcks Average number of mouse clcks Average number of left clcks Average number of rght clcks Average number of double clcks Average number of drag-anddrop Mean dwell tme Medan dwell tme Mnmum dwell tme Maxmum dwell tme Mean dwell tme Medan dwell tme Mnmum dwell tme Maxmum dwell tme Mean dwell tme Medan dwell tme Mnmum dwell tme Maxmum dwell tme Mouse Clck Features To the total number of mouse clcks Clcks per mnute Of all left or rght mouse clcks Of all left mouse clcks Of all rght mouse clcks B5.5
Mean transton tme Medan transton tme Mnmum transton tme Maxmum transton tme Mean transton tme Medan transton tme Mnmum transton tme Maxmum transton tme Table : Mouse Clck Features 3. Mouse Scroll Characterstcs Of all double clcks Of all drag-and-drop clcks Mouse scrollng refers to the turnng of the wheel n between the two buttons of the mouse. It can scroll up or down whle makng the applcaton move n that drecton respectvely. Table 3: Mouse Wheel Spn/ Scroll Features 4) MOUSE BIOMETRIC DATA In order for the key-logger to be runnng, the user has to go to the webste and log nto hs/her account or regster f they have not sgned up before. The user can pck Free Input from the drop-down menu and clck on the launch button to start. The followng 40 features shown n table 3 were mplemented [9]: Mouse Wheel Spn /Scroll Event Rato of scroll up Rato of scroll down Rato of tme spent n wheel events Mean duraton n seconds Medan duraton n seconds Mnmum duraton n seconds Maxmum duraton n seconds Mean duraton n seconds Medan duraton n seconds Mnmum duraton n seconds Maxmum duraton n seconds Mean duraton n seconds Medan duraton n seconds Mnmum duraton n seconds Maxmum duraton n seconds Mean scrolled dstance Medan scrolled dstance Mnmum scrolled dstance Maxmum scrolled dstance Mean scrolled dstance Medan scrolled dstance Mnmum scrolled dstance Maxmum scrolled dstance Mean scrolled dstance Medan scrolled dstance Mnmum scrolled dstance Maxmum scrolled dstance Mean speed (speed=dstance/tme) Medan speed Mnmum speed Maxmum speed Mean speed (speed=dstance/tme) Medan speed Mnmum speed Maxmum speed Mean speed (speed=dstance/tme) Medan speed Mnmum speed Maxmum speed Rato Mouse Wheel Spn/ Scroll Features To total number of wheel events To the total sample sesson tme For a wheel spn event For a scroll up event For a scroll down event For a scroll up or down event For a scroll up event For a scroll down event Of wheel spn events Of scroll up events Of scroll down events Of scroll up to scroll down events (default zero) Fg. 4. Webpage where key-logger app can be launched after loggng n. A fle s downloaded and opened wth Java Web Start. When ths fle runs t opens the key-logger and data can be seen beng outputted by the applcaton any tme the mouse cursor moves or the buttons are clcked. Fg. 5.Key-logger outputtng mouse data as the mouse moves or the buttons are clcked. Whle the applcaton s runnng, the bubble applcaton can be opened and the data from the user clckng on the bubbles wll be outputted to the key-logger applcaton. After the applcaton has attaned enough data from the sesson, the key-logger applcaton s ended and the data wll be uploaded nto the database. 5) TESTING THE MOUSE a) Bubble Clck Applcaton We have an applcaton n mnd that would make a bubble appear n a random spot on the screen, and the user s supposed to clck on the bubble. Some bubbles need to be clcked once and some dstngushable bubbles need to be clcked on twce. The bubble wll keep dsappearng as the user clcks on them untl the maxmum amount of B5.6
bubbles has been reached, whch s 0 bubbles. From the 0 bubbles that the user wll encounter, fve of them wll have an nner shaded crcle; these have to be doubleclcked by the user n order for the next bubble to appear. Fg. 6.Bubble comes out n a random part of screen, and after beng clcked appears n another part of the screen. As mentoned before, some of the bubbles wll be notceably dfferent from the rest and these have to be double-clcked as opposed to beng sngle clcked. They wll have an nner crcle that wll be shaded n. These are to test the user s double-clck speed as well as the velocty and the dstance that the mouse traveled. Is t yet another test to determne authentcaton accurately. The randomness of where the bubbles appear depends on a random generator that outputs a certan locaton on the screen. The random generator outputs a heght and a wdth output and these numbers are used to tell the bubble where on the screen to appear. The random generator also takes nto consderaton the tmestamp, so t s hghly unlkely that a bubble wll appear n the same exact spot as the prevous one. b) Bubble Data Gatherng Once the user has fnshed clckng on all of the bubbles and the key-logger has ended collectng the data, the data wll be uploaded nto the database where once we are granted access we can experment and run tests on the data usng the prevous formulas for determnng velocty, dstance, acceleraton, and angle change and can determne patterns or certan trats that we can use to authentcate the user. The bubble applcaton works by measurng a person s acceleraton and velocty once a bubble appears on the screen, t measures the user s clck speed when the user clcks on the bubble. The velocty and acceleraton are once agan tested when the clcked bubble dsappears and a new one appears on the screen. The double-clck speed of the user s tested when they encounter a bubble wth a shaded nner crcle. The double-clcked bubbles are to ensure authentcaton because certan users can have the same sngle-clck speed but by addng a double-clck test, the chances of t beng multple users greatly dmnshes. c) User Authentcaton A user s authentcaton can be determned by quantfyng the data nto measurable chunks that can be used to determne specfc characterstcs and trats. The user data s compared to logged data, and a match can be determned dependng on the smlartes between the unknown user and the data on the database. If a match s confrmed based on the velocty, acceleraton, dstance, and sngle and double-clcked speeds the user s authentcated and access s granted to the user. Ths s certanly another vable method for user authentcaton through a computer than just the common methods that we have seen n the past. d) Smlartes n Applcatons The bubble applcaton has some fun aspects about t because the user s expectng a bubble to appear but they do not know where t wll appear. The bubble applcaton has some smlartes to some well-known games, whch makes t a good method for user authentcaton. The fact that the user wll be actvely engaged n the bometrc nstead of dong t wthout any energy or desre means that they wll not get skewed results. The bubble applcaton s smlar to soltare n the sense that the user can clck on the deck to get the next card, the user can drag and drop cards, the user can double clck tems to put them on the stacks and the user has to move the mouse to dfferent parts of the screen before tme runs out. Fg. 7. Soltare applcaton showng the mouse clckng and draggng a card to another stack. 6) FUTURE STUDY Whle mouse dynamcs offers a mostly effectve authentcaton method n certan envronments, t may suffer from rather glarng ssues that prevent ts use on an end-user bass. One of the most problematc ssues s the length of tme that verfcaton requres n such mplementatons. Although studes menton successful verfcaton statstcs, the vast majorty of such studes fal to dentfy the exact length of the tmes used by such neural networks. In further analyss, t was determned that very few of the systems had an mplementaton tme of under %, whch hghly restrcts ther deploy ablty and marketablty to the consumer and commercal markets. Users are hghly pcky toward the wat tmes wth the use of verfcaton technologes, and a wat tme of over a B5.7
mnute would render such a system neffectve as there would be very few advantages f t prevents a natural computng workflow from occurrng. Whle ths may be negated through software nnovaton and dfferent modelng technques, t does not remove the realty that such methods requre statstcal computaton that s tedous to effectvely ntegrate and mplement. Furthermore, such systems cannot fully control varous varables that exst wthn a workng envronment, whch may sgnfcantly ncrease the rate of false postves n such systems. Users mght be usng dfferent mouse devces or dfferent hardware and the results can be nfluenced by those varables, and the authentcaton mght not be accurate [6]. Changes n workflows through dfferent machnes and tasks can render such analyss moot as dfferent workng envronments create a shft n behavor wthn the ndvdual. Ths unpredctablty wll not be able to be accounted for by the behavoral bometrc analyss technques, and would serve as an neffectve dentfcaton of the user. As such, nnovatons such as remote desktop use and the use of other nput technologes would render ths use moot. 7) CONCLUSION Whle ntally promsng, the mplementaton of mouse dynamcs suffers from varous theoretcal and physcal flaws that leave such a system as unusable and counterproductve n a workplace scenaro. Even when the system has demonstrated a sgnfcant level of success n dentfyng the user of a system, t s ether slow, nflexble to meet worker demand or s worthless as a stand-alone verfcaton system due to bult-n weaknesses. As such, even a fully mplemented system would functon best as a complement to a much more effectve system rather than as the prmary system tself. The fuson of dual bometrc systems mght seem lke a more vable method for authentcaton [10]. Combnng mouse bometrcs would yeld more accurate results, and t would be a seamless transton for the users because other perpherals lke the keyboard have to be used for normal computer use [6]. Ultmately, whle research has been nconclusve toward the role that mouse dynamcs wll play n verfcaton technques, there s sgnfcant doubt toward ts mplementaton and general vablty as a bometrcal system. Due to the shft n nput methods n recent tmes, such a system may be wholly outdated. [3] Wkpeda contrbutors. (013, October 16). Bometrcs [Onlne]. Avalable: http://en.wkpeda.org/wk/bometrcs [4] R. Kamnsky, M. Enev,E. Andersen, Identfyng Game Players wth Mouse Bometrcs. Unversty of Washngton, Seattle, WA, 008. Avalable: http://homes.cs.washngton.edu/~mro/docs/mouse_id.pdf [5] S. Hasha, C. Pollett, M. Stamp, On Usng Mouse Movements as a Bometrc.San Jose State Unversty, San Jose, CA,005. Avalable:http://www.cs.sjsu.edu/faculty/pollett/masters/Seme sters/sprng04/shvan/shvanpaper.pdf [6] Z. Jorgensen, T. Yu, On Mouse Dynamcs as a Behavoral Bometrc for Authentcaton.North Carolna State Unversty, Ralegh, NC, 011. Avalable: http://www4.ncsu.edu/~tyu/pubs/asaccs11- jorgensen.pdf [7] S. Sngh, K. Arya, Mouse Interacton based Authentcaton System byclassfyng the Dstance Travelled by the Mouse. Internatonal Journal of Computer Applcatons, vol. 17, pp. 45 47, March 011. Avalable:http://www.jcaonlne.org/volume17/number1/pxc3 8775.pdf [8] N. Zheng, A. Palosk, H. Wang, An Effcent User Verfcaton System va Mouse Movements.The College of Wllam and Mary, Wllamsburg, VA, 011. Avalable: http://www.cs.wm.edu/~hnw/paper/ccs11.pdf [9] P. Xaver de Olvera, V. Channarayappa, E. O'Donnel, B. Snha, A. Vadakkencherry, T. Londhe, U. Gatkal, N. Bakelman, J.V. Monaco, and C.C. Tappert, "Mouse Movement Bometrc System," Proc. CSIS Research Day, Pace Unversty, NY, May 013. Avalable: http://css.pace.edu/~ctappert/srd013/b.pdf [10] A. Ross, A. Jan, Informaton fuson n bometrcs. Mchgan State Unversty, East Lansng, MI, 003. Avalable:http://www.cse.msu.edu/rgroups/bometrcs/Publca tons/multbometrcs/rossjan_bometrcfuson_prl03.pdf REFERENCES [1] C. Brodley, M. Pusara, User Re-Authentcaton va Mouse Movements. ACM workshop on Vsualzaton and Data Mnng for Computer Securty (VzSEC/DMSEC '04), 004. Avalable:http://www.css.pace.edu/~ctappert/t691-11fall/projects/mouse-pusara.pdf [] Wkpeda contrbutors. (013, October 16). Mouse (computng) [Onlne]. Avalable: http://en.wkpeda.org/wk/mouse_%8computng%9 B5.8