An Enhanced Keystroke Biometric System and Associated Studies

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1 Procings of Stunt-Faculty Rsarch Day, CSIS, Pac Univrsity, May 2 n, 2008 An Enhanc Kystrok Biomtric Systm an Associat Stuis Tarjani Buch, Anra Cotoranu, Eric Jsky, Florin Tihon, Mary Villani Sinbrg School of CSIS, Pac Univrsity, Nw York, U.S.A. {tb43439w, ac73135w, j01113w, ft26714w@pac.u, villanmv@famingal.u Abstract Th Kystrok Biomtric Systm at Pac Univrsity intifis subjcts bas on long-txt (about 650 kystroks) sampls. Th systm consistntly intifis subjcts using th sam kyboar typ (sktop or laptop) an ntry mo (copy task or fr txt input) with grs of accuracy ranging from 98% to 100%. Th currnt work nhancs th prviously vlop kystrok biomtric intification systm an prforms aitional stuis. Aitional intification xprimnts support prviously ocumnt accuracy finings, an nw authntication stuis inicat promis for authntication application of th systm. Th fatur xtractor componnt is moifi to facilitat ths an futur xprimnts. Aitional ata sampls ar captur for controll longituinal stuis to vrify that rasonabl accuracy can b maintain with urations of svral wks btwn ata capturs. 1. Introuction Kystrok biomtric systms masur charactristics bliv to b uniqu to an iniviual an ifficult to uplicat [3]. Th Kystrok Biomtric Systm vlop at Pac Univrsity is a faculty originat projct. Th systm has bn nhanc by grauat stunts with th purpos of supporting faculty an octoral stunt rsarch in th ara of pattrn rcognition. Th systm was vlop for long-txt input applications such as intifying prptrators of inappropriat mail or impostr onlin tst takrs [3]. 2. Historical Rviw Th Kystrok Biomtric Systm projct start in 2004 an has sinc gon through four projct itrations with iffrnt grauat stunt tams. Th systm consists of a Java applt which collcts raw kystrok ata ovr th Intrnt. Following ata collction, longtxt-input faturs ar xtract, an a pattrn classifir maks intification cisions. Th first projct was a usr intification fasibility stuy that vlop th Java applt an initiat th work in this ara, primarily using th statistical SAS softwar for th vlopmnt of th fatur xtraction an classification programs. Th scon projct gnrat a complt systm in Java, which was not usr frinly an not succssfully appli. An improv thir systm, also in Java, was us in a fasibility stuy of a copy typing task. It was thn nhanc to support a stuy involving 118 subjcts, an using two input mos (copy an fr-txt input) an two kyboar typs (sktop an laptop kyboars). Th fourth systm mphasiz fallback mols which al with missing or insufficint statistical information, such as too fw instancs of infrqunt lttr kys. Two fallback mols wr vlop: on bas on linguistic an th othr on touch-typing principls. Th formr substituts incomplt or insufficint ata with mor gnraliz grammatical ata whil th lattr substituts incomplt or insufficint ata with gnraliz ata bas on th gography of a stanar computr kyboar. Ths projct itrations an rlat rsarch rsult in svral xtrnal publications as wll as in two octoral issrtations (s [3] an rfrncs thrin). Th currnt fifth projct xtns th arlir work as scrib blow. 3. Kystrok Systm an Enhancmnts Th transition from th fourth to th fifth systm itration focuss on th nhancmnt of th fatur xtraction componnt so that fatur ata can b us not only by th pattrn classifir of this systm but also by othr classifirs. Th kystrok biomtric systm consists of thr main componnts: raw kystrok ata collction, C4.1

2 fatur xtraction, an pattrn classification. Th systm mploys a Java applt to collct kystrok ata ovr th Intrnt, long-txt input faturs ar thn xtract, an a pattrn classifir maks intification cisions. Figur 1 prsnts th procss an ata flow for th kystrok biomtric systm. Figur 1: Kystrok biomtric systm procss an ata flow iagram Raw Kystrok Data Collction A subjct participating in th ata collction procss is rquir to rgistr at his/hr first visit to th systm wb sit. Th rgistration procss capturs mographic ata incluing: subjct s first an last nam, mail, country of schooling, nationality, nativ languag, ag, lvl of ucation, th mostly us kyboar typ, hannss, computr manufacturr, how subjcts larn to typ, an typing approach. To insur that all subjcts hav submitt mographic ata, ach tim a subjct rturns to th wb sit, h/sh is ask for his/hr first an last nam. A qury is thn run looking for th subjct in th atabas. If first nam an last nam ar foun, th subjct is takn irctly to th activity slction Wb form an is ask about th kyboar styl (sktop or laptop) h/sh is using an about what ntry task h/sh woul lik to prform a ata copy task or an xtmporanous task. Howvr, th fifth systm itration now asks all subjcts to complt ata ntry sampls for ach of th following tasks: copy-laptop, copy-sktop, fr-txt laptop, fr-txt sktop. This crats a mor complt basis for comparison xprimnts. A Java applt nabls th collction of kystrok ata ovr th Intrnt (Appnix 1). A submission numbr is automatically initializ an thn incrmnt aftr ach sampl submission. Th subjct can thn start typing his/hr ata sampl. If th subjct is intrrupt uring ata ntry, th Clar button will blank all fils, xcpt nam an submission numbr, an allow th usr to ro th currnt ntry. Th application raw ata fil rcors th following information for ach ntry: ky s charactr, ky s co txt quivalnt, ky s location (1 = stanar, only on ky location; 2 = lft si of kyboar; 3 = right si of kyboar), tim th ky was prss (milliscons), tim th ky was rlas (milliscons), an th numbr of lft-mous clicks, right-mous clicks, an oubl-lft-mous click vnts uring th sssion. Upon prssing submit, a raw ata txt fil limit by th ~ charactr is gnrat [3]. Prvious stuis by Villani t al. [3] inicat that, although all subjcts wr invit to participat in all four ntry tasks (copy-laptop, copy-sktop, fr-txt laptop, fr-txt sktop), tim or quipmnt limitations l som to opt for only two whil othrs participat in thr or four ntry tasks. Ultimatly only 36 subjcts complt all four ntry tasks. Furthrmor, in ths stuis th timing of th input sampls was not controll an ovr half of ths 36 subjcts ntr thir ata sampls in on sitting. Thrfor, for th currnt an subsqunt xprimnts all subjcts ar ask to complt fiv ata ntris for ach of th four ntry tasks an submit thm within a two ay intrval. In orr to arss th n for controll timing in ata collctions, thos subjcts who woul lik to complt aitional sts of four ntry tasks ar ask to lav approximatly two wks btwn sts. For th currnt stuis aitional ata sampls wr collct to support a controll longituinal stuy to trmin whthr rasonabl accuracis can b maintain with urations of svral wks btwn ata capturs. Data collction was schul as pr Tabl 1. Th tim intrvals, T 1, an T 2, inicat two wk intrvals. C4.2

3 Entry Task Tim T 1 T 2 Copy-Laptop Copy-Dsktop Fr-Txt Laptop Fr-Txt Dsktop Total # of Sampls Tabl 1: Data collction schul - on subjct Fatur Extraction Th fatur xtraction componnt, a Java application, ras all raw kystrok ata fils from a local irctory. On string of ata is crat from raw ata fils an stor in a vctor. Th vctor is ra in ascning orr from inx zro to inx N, whr N is th numbr of raw kystrok ata fils. A scon vctor is instantiat to track th frquncy of ach fatur tct from th raw ata. Th lowr lvl faturs ar simply th kys prss. Th highr lvl faturs ar pnant on th fallback mtho us in th analysis: linguistic or touch typ. Th faturs charactriz th typist s ky-prss, uration tims, transition tims in going from on ky to th nxt, th prcntags of usag of th non-lttr kys an, or mous clicks, an th typing sp. Thr ar a total of 254 faturs (s [3] for tails). In orr to support aitional intification an nw authntication xprimnts, th fatur xtractor was moifi to output ata in a stanar format with fils in comma limit rcors as follows (Figur 2): th first rcor contains th nam an scription of th fil; th scon rcor contains th numbr of sampls or pattrn instancs; th rmaining numbr of pattrn instancs ar contain in a rcor with th following fils: ID ata (.g. nam/gnr/ag), prson s application-rlat information (.g. hannss), quipmnt rlat information (kyboar typ), task prform (copy txt or fr txt), numbr of attributs/masurmnts, an squnc of fatur valus normaliz into th rang 0-1; 3.3. Pattrn Classification Aftr all faturs hav bn xtract into a faturs fil, th ata is ray to b classifi in an attmpt to intify th author. Th intification is a masur of th sum of th Euclian istancs of all th collct faturs. Th analysis can b on using on of two mthos. With th first mtho, train-on-on or lav-onout, on faturs fil is us an classification occurs by pulling out ach ata ntry an comparing it to all th othr ata in th faturs fil. Classification is succssful if th Euclian istanc is last with rspct to anothr ata ntry by th sam author. With th scon mtho of classification, train on on an tst on th othr, th classifir is train on on faturs fil an it attmpts to match th ata from a scon faturs fil to thos in th training fil. Again, a match in this cas is succssful whn, givn th sam author, th Euclian istanc is last btwn th ata bing tst an th ata in th training fil. 4. Exprimnts Biomtric systms hav intification an authntication applications. In intification applications a usr is intifi from within a population of n usrs (on of n rsponss). In authntication (vrification) applications, a usr is ithr accpt or rjct (binary rspons, ys you ar th prson you claim to b or no you ar not). Prvious projcts approach intification rlat problms rasoning that high rcognition accuracy woul yil systm succss. Th currnt projct supports both intification an authntication xprimnts. For classification purposs, th systm must b train bfor it bcoms usabl, so ata was sparat into two sts: on st for training th systm to crat bounaris in fatur spac an on for tsting th systm to trmin its accuracy. On way to support intification an authntication xprimnts is by using ata mining tchniqus. For this purpos, th projct was split btwn front-n an back-n componnts. Th front-n componnt manag ata gathring an fatur xtraction whil th back-n componnt manag classification procssing. Figur 2: Fatur vctor ata output. C4.3

4 4.1. Intification Exprimnts Exprimnts wr conuct by Villani t al. [3] on 36 subjcts who complt all four ata ntry tasks: sktop-copy, sktop-fr, laptop-copy, laptop-fr. Exprimntal rsults vrifi that th systm intifis subjcts with a high gr of accuracy if th subjcts us th sam kyboar typ (sktop or laptop) an ntry mo (copy task or fr txt task). Ths xprimnts tst optimal conitions (subxprimnts a an b), combin conitions (subxprimnt c) an lss optimal conitions (subxprimnts an ). All th xprimnts us th linguistic fallback mol. Exprimnts for optimal an combin conitions us th lav-on-out classification mtho. Ths xprimnts rval a vry high lvl of accuracy, ranging btwn 98.3% an 100%. Exprimnts for th lss optimal conitions us th train on on an tst on th othr classification mtho. Ths xprimnts rval a lvl of accuracy ranging btwn 50.3% an 91.7%. A complt st of xprimntal rsults on ths subjcts is prsnt in Tabl 2 [3]. Exprimnt Train Tst 1. Copy Task 2. Fr Txt 3. Dsktop 4. Laptop 5. Diffrnt Mo/Kyboar 6. Diffrnt Kyboar/Mo a Dsktop Dsktop 99.4% b Laptop Laptop 100.0% c Combin Combin 99.5% Dsktop Laptop 60.8% Laptop Dsktop 60.6% a Dsktop Dsktop 98.3% b Laptop Laptop 99.5% c Combin Combin 98.1% Dsktop Laptop 59.0% Laptop Dsktop 61.0% a Copy Copy 99.4% b Fr Txt Fr Txt 98.3% c Combin Combin 99.2% Copy Fr Txt 89.3% Fr Txt Copy 91.7% a Copy Copy 100.0% b Fr Txt Fr Txt 99.5% c Combin Combin 98.9% Copy Fr Txt 86.2% Fr Txt Copy 91.0% a Lap Fr Lap Fr 99.5% b Dsk Copy Dsk Copy 99.4% c Combin Combin 98.6% Dsk Copy Lap Fr 51.6% Lap Fr Dsk Copy 58.0% a Dsk Fr Dsk Fr 98.3% b Lap Copy Lap Copy 100.0% c Combin Combin 98.9% Lap Copy Dsk Fr 50.3% Dsk Fr Lap Copy 52.1% Tabl 2: Summary of rsults for 36-subjct intification xprimnts (from [3]). For this stuy, ths xprimnts wr rprouc for a four subjct st of ata collct accoring to th nhanc procss scrib in Tabl 1. W only rprouc thos xprimnts that tst th lss optimal conitions (sub-xprimnts an ). For th nw xprimnts, subjcts complt all four ntry tasks, an ach subjct submitt fiv sampls pr task at thr istinct tim intrvals,, T 1 an T 2. A total of 12 xprimnts wr run through th train on on an tst on th othr classification mtho, using th linguistic fallback mol, as pr th xprimnt summary scrib in Figur 3. Copy Task Fr Txt 3 Dsktop Laptop 5 Figur 3: Summary of xprimntal sign [3]. Th first hypothsis was that rsults from running ths xprimnts on th ata sts from th four nw subjcts will rval similar grs of accuracy to th xprimnts run on th 36 subjcts. Th grs of accuracy might b vn highr in th four-subjct xprimnts, givn that th subjct population is vry small. Th scon hypothsis was that high grs of accuracy will b maintain ovr tim from, through T 1, an T 2. Th rsults of our xprimnts support our first hypothsis an rval, as xpct, much highr grs of accuracy as compar to th 36-subjct intification xprimnts. Ths rsults ar most likly attribut to th vry small sampl siz (4. Rsults also confirm th scon hypothsis by rvaling that high grs of accuracy can b maintain ovr tim. Th gr of accuracy i not cras significantly ovr tim, an in som of th xprimnts it i not cras at all. Out of twlv xprimnts, fiv maintain 100% accuracy from to T 1 an furthr from to T 2. In fiv of th 4 C4.4

5 xprimnts, th lvl of accuracy cras from to T 1 to incras again at T 2. In on xprimnt, th lvl of accuracy incras from to T 1 an thn cras again at T 2. Furthrmor, in on xprimnt, th lvl of accuracy incras from to T 1 an furthr from to T 2. A complt st of xprimntal rsults on ths subjcts is prsnt in Tabl 3. Exprimnt Train/Tst Dsktop Laptop 40.56% Fr Txt Laptop Dsktop 52.84% Dsktop Copy Fr Txt 47.72% Fr Txt Copy 51.11% - - T 1 - T 2 Copy Fr Txt 18.89% 1. Copy Task (4 Dsktop/Lapto p 100% 85% 100% Laptop Fr Txt Copy 57.78% 2. Fr Txt (4 Laptop/Dskto p 100% 95% 100% Dsktop/Lapto p 100% 100% 100% Diffrnt Mo/ Kyboar Dsk Copy Lap Fr Lap Fr Dsk Copy 31.67% 55.56% 3. Dsktop (4 4. Laptop (4 5. Diffrnt Mo/ Kyboar (4 6. Diffrnt Kyboar/ Mo (4 Laptop/Dskto p 100% 100% 100% Copy/Fr Txt Fr Txt/Copy Copy/Fr Txt Fr Txt/Copy Dsk Copy/ Lap Fr Lap Fr/ Dsk Copy Lap Copy/ Dsk Fr Dsk Fr/ Lap Copy 85% 95% 85% 100% 100% 100% 100% 100% 100% 100% 90% 100% 90% 75% 100% 80% 95% 100% 95% 100% 95% 100% 100% 100% Avrag 96% 95% 98% Tabl 3: Summary of rsults for 4-subjct intification xprimnts. Th 36-subjct ata st has also bn us for intification xprimnts using ata mining tchniqus. Th xprimnt us Wka, a ata mining tool, with th k-narst-nighbor (IBk) algorithm with k=1 an th lav-on-out procur. Th rsults ar comparabl to th ons prsnt in Tabl 2 [2]. On xcption was th xprimnt which involv training on a laptop with a copy task an tsting on a laptop with a fr txt task; this xprimnt rval a vry low 18.9% accuracy rat as compar to th 86.2% in Tabl 2. Exprimnt Train Tst Dsktop Laptop 83.34% Copy Task Laptop Dsktop 51.67% Diffrnt Kyboar/ Mo Lap Copy Dsk Fr 38.07% Dsk Fr Lap Copy 54.45% Tabl 4: Summary of rsults for 36-subjct intification xprimnts (from [2]). Th intification xprimnts on th 4-subjct ata st focus on trmining grs of accuracy from a longituinal prspctiv. Ths xprimnts wr ran through Wka, using th IBk algorithm with k=1 an th lav-on-out procur. In ths xprimnts ata st was us for training whil th T 1 an T 2 ata sts wr us for tsting [2]. Train (5 sampls from ach (5 sampls from ach Tst T 1 (5 sampls from ach T 2 (5 sampls from ach Typ with Data Mining (Wka) with Kystrok Biomtric Systm Copy Dsk 95% 100% Fr Dsk 100% 100% Copy Lap 100% 100% Fr Lap 85% 100% Copy Dsk 80% 90% Fr Dsk 100% 100% Copy Lap 100% 100% Fr Lap 100% 100% Tabl 5: Summary of rsults for 4-subjct intification xprimnts [2] Authntication Exprimnts Th sam two sts of ata hav also bn us for authntication xprimnts. Exprimnts on th 36- subjct ata st wr run through Wka, using th k- narst-nighbor (IBk) algorithm with k=1 on th C4.5

6 ichotomy ata [1], an th lav-on-out procur [2]. Th grs of accuracy rang from 62.3%, whn training on a laptop with copy txt an tsting on a laptop with fr txt, to 97.3% whn training on a laptop with fr txt an tsting on a laptop with copy txt. To b notic is that ths grs of accuracy, th highst an th lowst, wr both achiv for thos xprimnts involving a laptop kyboar. A complt st of xprimntal rsults on th 36-subjct ata st is prsnt in Tabl 6 [2]. Exprimnt Train Tst Train (18 Tst (18 Copy Task Fr Txt Dsktop Laptop Dsk Copy Dsk Copy 87.94% Dsk Fr Dsk Fr 90.24% Lap Copy Lap Copy 91.03% Lap Fr Lap Fr 92.06% Dsktop Laptop 94.77% Laptop Dsktop 80.81% Dsktop Laptop 62.56% Laptop Dsktop 93.10% Copy Fr Txt 82.40% Fr Txt Copy 82.44% Copy Fr Txt 62.33% Fr Txt Copy 97.33% Diffrnt Mo/ Kyboar Dsk Copy Lap Fr Lap Fr Dsk Copy 62.44% 93.37% Diffrnt Kyboar/ Mo Lap Copy Dsk Fr 76.81% Dsk Fr Lap Copy 89.77% Tabl 6: Summary of rsults for 36-subjct authntication xprimnts (from [2]). Th authntication xprimnts on th 4-subjct ata st focus on trmining grs of accuracy from a longituinal prspctiv. Ths xprimnts wr run through Wka, using th IBk algorithm with k=1 on th ichotomy ata [1]. In ths xprimnts, th ata st was us for training whil th T 1 an T 2 ata sts wr us for tsting [2]. Th rsults yil onc again high grs of accuracy ranging from 88.9% to 100%. Ths xprimnts show that high grs of accuracy can b maintain ovr tim A complt st of xprimntal rsults on th 4-subjct ata st is prsnt in Tabl 7. Train (5 sampls from ach (5 sampls from ach Tst T 1 (5 sampls from ach T 2 (5 sampls from ach Typ Copy Dsk 95.79% Fr Dsk 96.32% Copy Lap 91.58% Fr Lap 92.11% Copy Dsk 88.95% Fr Dsk 98.42% Copy Lap % Fr Lap 93.68% Tabl7: Summary of rsults for 4-subjct authntication xprimnts (from [2]). 5. Conclusions an Rcommnations Prvious xprimntal rsults hav inicat high grs of accuracy in intifying subjcts bas on long-txt input, spcially unr th conitions whn th sam kyboar typ is us or th sam ata ntry task is prform. In nw xprimnts, our kystrok biomtric systm outputs fatur vctor ata in a format that nhancs intification procssing vn furthr an shows promising for authntication procssing. Although rsults of currnt stuis support prvious xprimntal rsults, it is rcommn that mor raw ata b collct following th prviously iscuss ata collction schul involving two wk intrvals btwn ata capturs. Running xprimnts with a largr ata pool collct unr th abov conitions shoul provi strongr vinc rlativ to th succss of th kystrok biomtric systm for intifying an vntually for authnticating subjcts. It woul also provi mor insight into how accuracy volvs from on ata collction sssion to anothr ovr tim. Nvrthlss, all xprimntal rsults ar promising in that th systm has th capability of solving intification problms an th potntial for solving authntication problms. C4.6

7 7. Rfrncs [1] S. Bharati, R. Hassm, R. Khan, M. Ritzmann an A. Wong, Biomtric Authntication Systm Using th Dichotomy Mol, Proc. CSIS Rsarch Day, Pac Univ., May [2] C. Eusbi, C. Gliga, D. John, an A. Maisonav, A Data Mining Stuy of Mous Movmnt, Stylomtry, an Kystrok Biomtric Data, Proc. CSIS Rsarch Day, Pac Univ., May [3] M. Villani, C. Tapprt, G. Ngo, J. Simon, H. St. Fort, an S. Cha, Kystrok Biomtric Rcognition Stuis on Long-Txt Input unr Ial an Application-Orint Conitions, Procings of th Confrnc on Computr Vision an Pattrn Rcognition Workshop, Fbruary Appnix 1 : Java Applt import javax.swing.*; import java.applt.applt; import java.awt.*; import java.awt.vnt.*; //import java.scurity.*; //import java.rmi.*; public class KySp16_Applt xtns Applt implmnts ActionListnr { privat int APPLET_WIDTH = 700; privat int APPLET_HEIGHT = 420; privat KySp16 kysp; public voi init() { java.nt.url url = this.gtdocumntbas(); String qury = url.gtqury(); //qury = "Hug&Hort&Fabl&1&kb&pc"; // tst purposs try { String[] usr = qury.split("&"); kysp = nw KySp16(usr[0].toUpprCas(), usr[1].toupprcas(), usr[2].toupprcas(), usr[3].toupprcas(), usr[4].toupprcas()); a (kysp); stsiz (APPLET_WIDTH, APPLET_HEIGHT); catch (Excption ) { public voi actionprform (ActionEvnt vnt) { //Systm.stScurityManagr(nw RMIScurityManagr()); C4.7