Mouse Dynamics Verification Technique

1. “User Identity verification via mouse dynamics”
Under the Guidance of –
Prof. D.V. Kodavade
Head & Associate Professor, Department of CSE,
D.K.T.E Ichalkaranji, Kolhapur.
Sumitted By –
Mr. Gorad Balwant Jaywant
M.Tech –II(CST),
Department of Technology, Shivaji University, Kolhapur.

2. Index
Introduction
Choice of the topic
Literature Review.
System Architecture
System Requirement and Design
Implementation
Experiments and Results
Conclusion and Future Enhancements
Bibliography
List of Journals and Publications
2

3. Obviously,EveryoneknowsabouttheHacking,anditisacrime, Becausenoonewantstoshareallhisprivatedatawithpublic.
Andtodayssystemsarenotguarrentingthefullsecurity,Hackerscaneasilystealthecredentialsofcomputerbyusingvarioustechniquessuchasphishingattack,keyloggersandmanymoredifferentattacks.
Thismethodgivesonemoresecuritylayerwithadditiontotheexistingcredentialsofthesystem,soitprovidesbettersecuritytothecomputers.
1. Introduction-
3

4. .
Thedrawbackofnormalidentificationmethodsthatarebasedonlyoncredentials,leadstotheintroductionofuserauthenticationandverificationtechniques,thatarebasedonbehavioralandphysiologicalbiometricswhichareassumedtobeuniquetoeachotherandhardtosteal.
Soforgoodsecurityweshouldperformauthenticationaswellasverification.
Inthissystem,authenticationisperformedonceduringthelogintothecomputerwhileverificationisperformedcontinuouslythroughoutthesessionbydrawinghis/herprivatemousedynamics.
Followingtableshowssomeofbiometrictechniquesandtheiraccuracies.
4

5. Biometric Technology
Accuracy
Cost
Device Required
Acceptability
Iris Recognition
High
High
Camera
Medium-low
Retinal Scan
High
High
Camera
Low
Face Recognition
Medium- low
Medium
Camera
High
Voice Recognition
Medium
Medium
Microphone
High
Finger Print
High
Medium
Scanner
Medium
Signature Recognition
Low
Medium
Mouse, Optic Pen, Touch Panel.
High
Hand Geometry
Medium-low
Low
Scanner
High
Table No. 1 Overview of Biometric Technologies
5

6. .
CurrentlymostofthecomputerSystemsandonlinewebsitesidentifiestheusersbymeansofusernamesandpasswords/ PINS.Butnormallyhackerscaneasilystealthepassword.
Therearesomanytechniqueswhichareusedtohacktheusernameandpasswordsofthesystems.Someofthetechniquesarephishing,keyloggersandmanymore.
Sothereisneedtoimprovesecuritylevelofexistingcomputers.
Thisproposedapproachgivesonemoreadditionalsecuritylayertotheexistingsecuritylayerwhichusesmousedynamicsverification.
2. Choice of the topic-
6

7. .
Userverificationcanbeoftwotypes–eitheritphysiologicalorbehavioral.
Thedrawbackofphysiologicalverificationmethodsisthattheyrequirededicatedhardwaredevicessuchasfingerprintsensorsandretinascannerswhichareexpensiveandarenotalwaysavailable.
ButBehavioralbiometrics,ontheotherhand,donotrequirespecialdesignatedhardwaresincetheyusecommondevicessuchasthemouseandkeyboard.
Mouseverificationcanbeusedeffectivelythankeyboarddynamics,souseridentityverificationusingmousedynamicsisselectedforproposedwork.
7

8. .
Mostcommonbehavioralbiometricsverificationtechniquesarebasedon:
(a)mousedynamics[1][2][8],whicharederivedfromtheuser-mouseinteractionandthefocusofthisimplementationisbasedonmousedynamicsoftheuser;
(b)keystrokedynamics[7][10],whichderivefromthekeyboardactivity;suchfrequencyofkeypressing,typingspeed,etcand
(c)softwareinteraction,whichrelyonfeaturesextractedfromtheinteractionofauserwithaspecificsoftwaretool.
3. Literature Review-
8

9. .
3.1MouseBasedApproaches
ThistypeofAuthenticationmethods,identifiesusersatloginbasedonapredeterminedsequenceofmouseoperationsthattheuserneedstofollow.
Duringtraining,thefeaturesofmouseoperationfortheparticularuserisstored.Thesefeaturesareusedtocharacterizetheuserduringtheverification.Duringverification,theuserisrequiredtofollowthesamesequence.
Twotypesofmousebasedapproacheswehave-
9

10. .
3.1.1.Explicitlearningmethods-AuthorHashia[13] usedasequencecomposedofpairsofpoints.Eachuserwasrequiredtomovethemousebetweenthefirstandsecondpointineachpairwherefeatureswereextractedfromeachmovement.
ThemethodproposedbyGamboa[12]requiredtheuserstoenterausernameandapinnumberusingonlythemouseviaanon-screenvirtualkeyboard.Authenticationcombinedthecredentialsandthemousedynamicsoftheirentry.
3.1.2.Implicitlearningmethods-PusaraandBordley[15] explainedamethodtodetectanomalousbehaviorusingthecurrentuser'smousemovements.
10

11. .
3.2KeyboardandSoftwareApproaches-
Alternativeapproachestouserverificationutilizekeyboarddynamicsandsoftwareinteractioncharacteristics.
Ling,Luiz[7]andChanandHan[10]implementedmethodsbasedonkeyboarddynamics,forexample,featuresconsideredarelatencybetweenconsecutivekeystrokes,typingspeed,flighttime,dwelltime-allbasedonthekeydown/press/upevents.
Keyboard-basedmethodsaredividedintomethodsthatanalyzetheuserbehaviorduringaninitialloginattemptandmethodsthatcontinuouslyverifytheuserthroughoutthesession.
11

12. Beforethediscussionofproposedsystemanditsarchitecture,letusdiscusssomethingaboutgeneralbehavioralbiometricssystem.
Abiometric-systemisessentiallyapatternrecognitionsystemthatacquiresbiometricdatafromanindividual,extractsafeaturesettoestablishauniqueusersignatureandconstructsaverificationmodelwhichclassifiesauthenticateduserandnonauthenticateduser.
Fig.1showsthegeneralbehavioralbiometricsystem4. System Architecture- 12

13. Suchsystemsincludethefollowingcomponents:
Featureacquisition–capturestheeventsgeneratedbythevariousinputdevicesusedfortheinteraction(e.g.Keyboard, mouse)viatheirdrivers.
Featureextraction–constructsasignaturewhichcharacterizesthebehavioralbiometricsoftheuser.
SimilarityMatch/DecisionTaker–Thisisusedtobuildtheuserverificationmodel,whichwilltakeadecisionabouteithercomputersystemwillshutdownoritwillcontinuethework. Duringverification,thismodelisusedtoclassifynewsamplesacquiredfromtheuser.
Signaturedatabase–Adatabaseofbehavioralsignaturesthatwereusedtotrainthemodel.Uponentryofausername,thesignatureoftheuserisretrievedfortheverificationprocess.
13

14. 4.1 The Proposed System ArchitectureFigure.2 Architecture of Proposed System
14

15. Thesystemisclassifiedmainlyintofourcomponents,whichareasfollows.
4.1.1Featureacquisition–Systemcapturestheeventsgeneratedbythevariousinputdevicesusedfortheinteraction(e.g.Keyboard,mouse)viatheirdrivers.ThisApproachtotallyprefersmouseinteractionwithcomputersystemsasshowninfig.2.
(i)Mouse-moveEvent(m)(ii)LeftButtondown(ld)
(iii)RightButtondownEvent(rd)(iv)LeftButtonup(lu)
(v)RightButtonupEvent(ru)(vi)Silence(s)
15

16. .
4.1.2.FeatureExtraction–constructsasignaturewhichcharacterizesthebehavioralbiometricsoftheuser.Pleasereferfig.2togetoverallideaoffeatureextractionfromusersmousedynamics.
Higherlevelfeaturesincorporatedependenciesbetweenlower-leveloneswhichhelptocharacterizemoreaccuratelyeveryuser.
ForExample,amouseleftclickcontainstwolowleveleventssuchasleftdownandleftup.
Secondexamplewewouldliketogivethat,MMS(MouseMoveSequence)iscomposedofmultiplemousemoveeventsinbetweensilenceintervalispresent.
16

17. .
In the proposed hierarchy, Following are the features are considered for extraction.
LeftClicks(LC)
RightClicks(RC)
DoubleClicks(DC)
MouseMove(MM)
AreaunderCurve(AUC)
Eccentricity(ECC)
TotalTime(TT)
17

18. I. Left Clicks (LC) – refers to the action of clicking on the left
mouse button. This action consists of a left button down event
followed by a left button up event taking place within specified
τLC seconds from the button down event.
Formally,
Where ld = left down, lu = left up, m1, m2 ...mn = mouse
move events and τLC = specified time interval
Fig. 3 Left Click feature
18

19. II. Right Clicks (RC) – refers to the action of clicking on the
right mouse button. This action consists of a right button down
event followed by a right button up event taking place within
specified τRC seconds from the button down event.
Formally,
Where rd = right down, ru = right up, m1, m2 ... mn = mouse
move events and τRC = specified time interval
Fig. 4 Right Click feature
19

20. III. Double Clicks (DC) - is composed of a two consecutive
left clicks or right clicks in which the mouse-up of the first click
and the mouse-down of the second one occur within an
interval of τI seconds.
Formally:
Fig. 5 Left Click feature
20

21. IV. Mouse Move (MM) - A sequence of mouse-move events
followed by silence time σ.
Formally,
MM = MMS.σ
Fig. 6 Left Click feature
21

22. V.AreaUnderCurve(AUC)–Actualnumberofpixelsintheregion.
Theinitialvalueofpixelis0;ThatiscurrentlyArea=0;
Formally,
Current Area = Current Area + 100/(Image Height * Image Width)
Pixels = Image.getPixel(x1,y1)
Where x1 < Width of image and y1 < Height of Image
22

23. VI.Eccentricity(ECC)–
Theratioofthedistancebetweenthefocioftheellipseanditsmajoraxislength.
Eccentricityofanellipseisameasureofhownearlycirculartheellipse.Itisfoundbyfollowingformula,
Eccentricity (ECC) = C/A
WhereCisthedistancefromthecentertofocusoftheellipseandAisthedistancefromcentertovertex.
Fig. 7 Eccentricity
23

24. VII.TotalTime(TT)–Thisfeaturecalculatestheapproximatetimerequiredtodrawamousedynamicstothetrustedthirduser.StandardtimerisusedintheC#languagetocalculatethetimerequiredtodrawamousesignature.ForExampletimerstartswhentherespectiveformloadsanditstopswhenwepresstheExtractbuttonwhichispresentonstandardGUI.
SototaltimerequiredtodrawasignaturecanbeCalculatedisasfollows
Total Time (TT) = T2-T1
Where
T2=TimeWhenwefinishtheSignatureAnd
T1=TimewhenwestartthedrawingSignature(Whenformloads). 24

25. 4.1.3.SimilarityMatch/DecisionTaker–
Thisisusedtobuildtheuserverificationmodelbyusingaconsiderablethreshold.Duringverification,thismodelisusedtoclassifynewsamplesacquiredfromtheuser.
Asweknowwecan‟tdrawthesamesignatureeverytimewithapenalso,soit‟sverydifficulttodrawthesamesignaturebymouseintothecanvas,Sothresholdplaysanimportantroleinthisapproach.
AClassifier/similaritymatchtakesthedecisioneithersystemhastocontinuetheloginorlogoutbasedonasimilaritymatchbetweentheuserdynamicsdrawnduringtheregistrationandduringtheverification.
ThiscomponenttakesthevalueofthepercentageofMatching(POM)fromthepreviousstepanddecideseithercomputerwillshutdownoritwillcontinuetheloginasshowninfig.2
25

26. So, Percentage of Match (POM) is calculated with the help of following formula.
Final Percentage of Matching (POM) =
POM (in LC) + POM (in RC) + POM (in DC) + POM (in MM) + POM (in AUC) + POM (in ECC) + POM (in TT) .
Another Factor used in Classifier is PVM(Predefined Value Set for Matching), This can be decided by administrator of the system
PVM is the criteria to set the security level.
IF POM≥ PVM… User Access to computer is Granted
Else User Access is Denied26

27. Thiswholeprocessshouldperformmultipletimes,sothattrustedthirdpartieswillgetmorechancestoprovehis/herauthenticationandillegaluserswillhavemoredifficultiestoprovehe/sheisanauthorizedmultipletimes.
Thefinaldecisiontakenbythedecisiontaker(eitheritisauthenticatedornotauthenticated)willgetthedecisiononhis/herregisteredmobile.Alsowhatactiontookbythesystemitwillalsobeconveyed,Actionmaybecomputersystemremainsloginoritisgoingtoshutdown.
FollowingtwoConditionsmaybethere.
Condition
Message on Mobile
Action Taken by Decision Taker
Table2. Decision Table
27

28. Software Requirements
Operating System:
Windows 2000/XP/2003/Vista/7/8
Microsoft Visual Studio 2008, 2010
(MS VS2010 Recommended)
Microsoft SQL Server 2005/ 2008
(2008 Recommended)
Microsoft Visio 2010 suite.
Hardware Requirements
Minimum Requirements:
Intel Pentium 4 & above
1 GHz processor
512 MB RAM
Recommended system:
Intel Core i3 or Above processor,
4 GB RAM or Above
Hard Disk Drive 320 GB
Optical Mouse (Recommended)
Intel processor is recommended for better performance.
5. SYSTEM REQUIREMENTS AND DESIGN
28

29. 5.1 Design using Dataflow diagrams
Fig. 8 Data flow diagram for registration
Fig. 9 Data flow diagram for
verification
29

30. 5.2ActivityDiagram
Fig. 10 Activity diagram of registration process
Fig. 11 Activity diagram of verification process
30

31. 5.3ProjectFlowDiagram
Fig. 12 Project flow diagram
31

32. 6.IMPLEMENTATION
TheimplementationoftheproposedsystemiscarriedoutusingC#programminglanguageandbyusingMicrosoftVisualStudio2010editor.
6.1.ImplementationofMouseDatabase
Thedatabaseusuallycontainsunlimitedtablesandinonetableusuallycanstoreunlimitedusers.Alongwithuserstheirmousesignaturefeaturesarealsomaintained.
Duringtheverificationphasethestoredfeaturesofparticularuserscanberetrievedforverificationwiththehelpoftheusername. Soit‟smandatorytogiveuniqueusernameduringtheregistrationphase.
ThedatabasecanbecreatedwithMicrosoftSQLServer2008whichisinbuiltinvisualstudio2010.
32

33. Table No
Name of database
Table names
Name of the columns
Purpose of creation
1
MouseDB
AddUserTable
Id, username, password,
Mobile, Signature, question, ans
To Add New User into System.
2
MouseDB
feature
User_name, Area, Double_clk, Eccentricity, mouse_mvc, Total_time,Left_clk, Right_clk
To Store the features those are extracted from user drawn mouse dynamics / mouse signature.
3
MouseDB
chk
chk, match
To check valid matching or invalid matching.
4
MouseDB
count1
count, validity
To check number times valid verification and the number of times invalid verification.
5
MouseDB
MainLogin
UserNm,UserPass
To access the system, its main login to system.
6
MouseDB
temp
username, path
To store the mouse signature image path.
Table3. Database tables
33

34. 6.2GraphicalUserInterfaceImplementation
Theprojectentitled“UserIdentityVerificationviamouseDynamics”isdividedintoseveralmodulesasweconsiderforimplementationsuchasRegistrationofuser,DrawingSignature, ExtractingtheFeaturesandStoringsignatureinMouseDatabase, UserVerification,DecisionTaking,etc.
FollowingvideowillshowsustheGUIofthissystemandhowthissystemwillwork.
34

35. 35

36. 7. EXPERIMENTS AND RESULTS
Experiment 1-
Thisfirstexperimentisconductedtotesttheauthenticationandnonauthenticationfortherespectiveusers.
Obviouslyiftheuserisabletodrawthesamedynamicsthenandthenonlyuserwillbeauthenticatedelseitisnotauthenticated.
SameUsername,Password,MobileNumberandFavoriteNumberareusedtoconducttheexperiment. Fig. 13 Sign during registrationFig. 14 Sign during verification36

37. Sr. No
Features Extracted
Value During Registration
Value During Verification
Final Decision
1.
Left Clicks (LC)
14
03
16% Match
Not Authenticated User
2.
Right Clicks (RC)
6
0
3.
Double Clicks (DC)
4
0
4.
Mouse Move (MM) pixels
1147
1013
5.
Area Under Curve (AUC) pixels
31562
22801
6.
Eccentricity (ECC)
0.4778
-0.0957
7.
Total Time (TT) Seconds
19
17
Table.4 Result of experiment 1
37

38. Experiment2-
Thissecondexperimentisconductedtotesttheauthenticationandnonauthenticationfortherespectiveusersiftheyaredrawingsamesignature.
Obviouslyiftheuserisabletodrawthesamedynamicsthenandthenonlyuserwillbeauthenticatedelseitisnotauthenticated.Alsosameusername,password,mobilenumberandfavoritenumberareusedtoconducttheexperiment.
Fig. 15 Sign during registrationFig. 16 sign during verification
38

39. Table.5 Result of experiment 2
Sr. No
Features Extracted
Value During Registration
Value During Verification
Final Decision
1.
Left Clicks (LC)
14
14
88% Matched
Authenticated user
2.
Right Clicks (RC)
6
6
3.
Double Clicks (DC)
4
4
4.
Mouse Move (MM) pixels
1147
1085
5.
Area Under Curve (AUC) pixels
31562
31320
6.
Eccentricity (ECC)
0.4778
0.5172
7.
Total Time (TT) Seconds
19
18
39

40. Experiment3
Thisisageneralexperiment,inthisexperimentdifferentpossibilitiesofsignaturedrawingareconsidered.Asetofsignatureshasbeentakentotestitwithstoredsignatureinadatabase.Insuchcasesthematchinggivesasimilarityvaluedependsonhowthesignatureisdrawnbytheuser.
Fig.17 Registered user signature for experiment 3
40

41. Sr.
No
User Signatures
% of Match
Sr.
No
User Signatures
% of Match
1
28%
4
88%
2
88%
5
43%
3
85%
6
82%
Fig.18 General Experiment with results
41

42. 8.CONCLUSIONANDFUTUREENHANCEMENTS
8.1Conclusion
Anovelmethodforuserverificationbasedonmouseactivityisimplementedinthiswork.CommonmouseeventsperformedinaGUIenvironmentbytheuseriscollectedandahierarchyofmouseactionsisdefinedbasedontherawevents.
Inordertocharacterizeeachaction,featuresareextracted. Atwo-layerverificationsystemisimplemented.Thesystememploysafeatureextractioninitsfirstlayerandadecisionmoduleinthesecondoneinordertoverifytheidentityofauser. Theimplementedmethodisevaluatedusingadatasetthatiscollectedfromavarietyofusersandhardwareconfigurations.
42

43. Asperexperimentsconducted,betteraccuracyisachievedthanhistogramtechnique.Theobservationinexperiment3.2,3.3,3.4and3.6,showsthatbetteraccuracyisobservedwhentherespectiveuseristryingtobehaveasthesamewhathebehavedduringtheregistration.
Inexperiment3.2,theachievedaccuracyis88%,Experiment3.3itis85%,Experiment3.4itis88%andExperiment3.6itis82%.Asperexperimentsconductedexperiment1,3.1and3.5, accuracyiscollapsingifusertriestomisbehave,whichisshowninresultsofexperiment1,theachievedaccuracyis16%,
Experiment3.1accuracyis28%andExperiment3.5accuracyis43%,whichislessthanthepredefinedthreshold,henceitisasigntothecomputersystemthatitwillnolongercontinue.
43

44. 8.2FutureEnhancements
Inthefollowingwedescribeseveralissuesthatneedfurtherinvestigationinmouse-basedverificationmethods.
Theoriginalactionsintendedbytheuserareloggedneitherbysoftwarenorbyobservingtheuserwhileperformingtheactions. Accordingly,theyareheuristicallyreconstructedfromtheraweventswhichmayproducesomenon-credibleactions.
Additionally,theobtainedactionsmayvarybetweendifferenthardwareconfigurations(e.g.Opticalmouse,touchpad).Inordertoobtainahigherpercentageofcredibleactions,theparametersthatdefinethemshouldbedeterminedbyamorerigorousmethod.
44

45. 8.3Applications
Duetotheadvancesintechnology,itisquiteeasytocrackthesecuritysystemsavailabletoday.Biometricsistheonlymechanismwhichiscomparativelymoresecurethanothertraditionalmethods.
Alsoitprovidesonemoreadditionalsecuritylayertotheexistingsecuritylayer.Thissystemaimedatimprovingthesecurityofthebiometricsystemthatusesmousedynamics/mousesignaturefeatures.Theapplicationsofthissystemarenotlimitedtoaspecificarea.
Someoftheapplicationsareasfollows.
Bankingsector,Anykindofelectronicdevices-FromdesktopcomputerstoPDAs,Mobiletopalmtops,Researchlaboratories, Electronicvotingmachines,ATMcounters,Emailsandmanymore..
45

46. 9.BIBLIOGRAPHY
9.1DocumentReferences
1.ClintFeher,YuvalElovici,RobertMoskovitch,LiorRokach,AlonSchclar,“Useridentityverificationviamousedynamics”,InformationSciences201(2012) 19–36.
2.ChaoShen,ZhongminCai,XiaohongGuan,YoutianDu,andRoyA.Maxion, UserAuthenticationThroughMouseDynamics.IEEETRANSACTIONSONINFORMATIONFORENSICSANDSECURITYVOL.8,NO.1,Jan2013
3.ZachJorgensenandTingYu,OnMouseDynamicsasaBehavioralBiometricforAuthentication.ACM978-1-4503-0564-8/March2011.
4.Z.Jorgensen,T.Yu,“Onmousedynamicsasabehavioralbiometricforauthentication,in:ProceedingsoftheSixthACMSymposiumonInformation, Computer,andCommunicationsSecurity”(AsiaCCS),March2011
5.M.DeMarsico,M.Nappi,D.Riccio,G.Tortora,NABS:“novelapproachesforbiometricsystems,IEEETransactionsonSystems,Man,andCybernetics”,PartC:ApplicationsandReviews41(4)(2011)481–493.
46

47. 6.SaurabhSingh,DrKVArya,“MouseInteractionbasedAuthenticationSystembyClassifyingtheDistanceTraveledbytheMouse”InternationalJournalofComputerApplications(0975–8887)Volume17–No.1, March2011
7.LíviaC.F.Araújo,LuizH.R.SucupiraJr.,MiguelG.Lizárraga,LeeL.Ling,andJoãoB.T.Yabu-Uti,“UserAuthenticationthroughTypingBiometricsFeatures,IEEETransactionsonSignalProcessing”,Vol.53,No. 2,February2005
8.P.Bours,C.J.Fullu,“Aloginsystemusingmousedynamics,in:FifthInternationalConferenceonIntelligentInformationHidingandMultimediaSignalProcessing”,2009,pp.1072–1077.
9.S.Bleha,C.Slivinsky,B.Hussein,“Computer-accesssecuritysystemsusingkeystrokedynamics,IEEETransactionsonPatternAnalysisandMachineIntelligence”12(12)(1999)1217–1222.
10.S.Cho,C.Han,D.H.Han,H.I.Kim,“Web-basedkeystrokedynamicsidentityverificationusingneuralnetwork,JournalofOrganizationalComputingandElectronicCommerce”10(4)(2000)295–307.
11.L.Ballard,D.Lopresti,F.Monrose,“Evaluatingthesecurityofhandwritingbiometrics,in:The10thInternationalWorkshoponFrontiersinHandwritingRecognition”(IWFHR„06),LaBaule,France,2006.
12.H.Gamboa,A.Fred,“Anidentityauthenticationsystembasedonhumancomputerinteractionbehavior,in: 3rdInternationalWorkshoponPatternRecognitiononInformationSystems”,2003,pp.46–55.
13.S.Hashia,C.Pollett,M.Stamp,“Onusingmousemovementsasabiometric,in:ProceedingintheInternationalConferenceonComputerScienceanditsApplications”,vol.1,2005.
14.A.A.E.Ahmed,I.Traore,“Anewbiometrictechnologybasedonmousedynamics,IEEETransactionsonDependableandSecureComputing”4(3)(2007)165–179.
15.MajaPusara,CarlaE.Brodley,“UserRe-AuthenticationviaMouseMovements”,SEC/DMSEC'04,October29,2004,Washington,DC,USA.Copyright2004ACM1-58113-974-8/04/0010
47

48. 9.2 Web References
[W1]. http://www.google.co.in/
[W2]. http://www.csharpcorner.com/
[W3]. http://www.stackoverflow.com/
[W4]. http://www.wikipedia.com/
[W5].http://www.codeproject.com/
48

49. 10. LIST OF JOURNALS AND PUBLICATIONS
1. “User Identity Verification Using Mouse Signature” in the International Organization of Scientific Research(IOSR)e-ISSN: 2278-0661, p-ISSN: 2278-8727Volume 12, Issue 4 (Jul. -Aug. 2013).
49

50. Thank You Very Much
50