global trends in online fraud (2016) · 2017. 10. 6. · accuracy and being able to deal with...

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WHITEPAPER|InvisibleChallenges:BioCatch’sGame-ChangingTechnologyforOnlineFraudPrevention

WhitePaper

GLOBALTRENDSINONLINEFRAUD(2016)UseCasesforBehavioralBiometricsintheBankingIndustryMarch2017

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TableofContents

ExecutiveSummary..................................................................................................................3

IntroductiontoBehavioralBiometrics&InvisibleChallenges.................................................2

RotationofMovement.........................................................................................................5

SpinningWheel....................................................................................................................6

DisappearingMouse.............................................................................................................7

InvisibleChallenges,InvincibleChallenges...............................................................................8

ResultsandConclusion–LessFriction.LessFraud................................................................10

AboutBioCatch......................................................................................................................11

CopyrightThiscontentiscopyrightofBioCatchTM2017.Allrightsreserved.Anyredistributionorreproductionofpartorallofthecontentsinanyformisprohibitedotherthanthefollowing:

• youmayprintordownloadtoalocalharddiskextractsforyourpersonalandnon-commercialuseonly• youmaycopythecontenttoindividualthirdpartiesfortheirpersonaluse,butonlyifyouacknowledgethe

documentasthesourceofthematerialYoumaynot,exceptwithourexpresswrittenpermission,distributeorcommerciallyexploitthecontent.Normayyoutransmititorstoreitinanyotherwebsiteorotherformofelectronicretrievalsystem.

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ExecutiveSummary

BioCatchisacybersecuritycompanythatdeliversbehavioralbiometrics,analyzinghuman-deviceinteractions,toprotectusersanddata.BanksandotherenterprisesuseBioCatchtosignificantlyreduceonlinefraudandprotectagainstavarietyofcyberthreats,withoutcompromisingtheuserexperience.OneofthekeyaspectsthatdistinguishesBioCatchasthemarketleaderinbehavioralbiometricsisitspatentportfolio,whichasofthiswritingismadeupof46patents,17ofthemgrantedorpublic.Amongthem,isagroupthatpertaintoacapabilitycalled“InvisibleChallenges™”.InvisibleChallengesrefertoteststhatareinvokedintoanonlinesessionwithouttheuser’sknowledge,butthatelicitsubconsciousresponsesthatcanbeusedtodistinguishafraudsterfromalegitimateuser.Thispowerfulmechanismrepresentsthelatestgenerationoffraudpreventiontools,thataddressestheweaknessoftraditionalapproachesthatrelyonmalwarelibraries,two-factorauthentication,deviceIDandothermeansthatthesophisticatedfraudstersoftodayhavefiguredouthowtocircumvent.InvisibleChallengesalsoseparatesBioCatchfromotherbehavioralbiometricsprovidersthatarefocusedontraditionalkeyboard,mousemovementsandgestureanalysis,intermsofaccuracyandbeingabletodealwithdifferenttypesofreplayattacks,humaninteractionsimulationandadvancedmalwareinjections.

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IntroductiontoBehavioralBiometrics&InvisibleChallenges

TheBioCatchsystemauthenticatesusersbywhotheyare,ratherthanbywhattheyknow(e.g,passwords,securityquestions).Employingcutting-edgebehavioralbiometrictechnology,thesystemanalyzesmorethan500differentbehavioralpatternsduringasession(post-login)todeterminewhethertheuserisinfactthegenuineuserandnotahuman/non-humanimposter.Theseparametersinclude:

• Cognitivefactorssuchaseye-handcoordination,applicativebehaviorpatterns,usagepreferencesanddeviceinteractionpatterns.

• Physiologicalfactorssuchasleft/righthandedness,press-size,handtremors,armsizeandmuscleusage.

• Contextualfactorssuchastransaction,navigation,deviceandnetworkpatterns.Eachuserprofileisbasedonthe20parametersthataremostuniquetothem.Aftercomparingthesessiondatatothegenuineuser’sprofile,BioCatchprovidesariskscoreinreal-timethatcanbeusedasastandaloneindicatororincombinationwithotherthreatdetectionsystems.Oursolutionisdesignedtoreducefrictionassociatedwithauthentication,savecostsassociatedwithescalationstocost-centersbecauseoffailedauthenticationsandfalsealarmsandreduceoverallfraudbyrecognizingfraudsterbehaviorasopposedtofixedmeansofidentitywhichmaybelost,stolenorcircumvented.Attheheartofwhatmakesthispossiblewithveryhighaccuracy,aretheInvisibleChallenges.Thesearepatentedtechniquesthatintroducesubtletestsintotheonlinesessionthatuserssubconsciouslyrespondtowithoutsensinganychangeintheirexperience.Theresponsecontainsbehavioraldatathatisusedtodistinguisharealuserfromanimposter,whetherhumanornon-human(roboticactivity,malware,aggregator,etc.).ItisimportanttonotethatBioCatch’steamofresearcherstesteachchallengeanditscorrespondingdeviationtodeterminethethresholdatwhichusersnoticeachangeinexperienceonthemobileorwebsite.

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ThefollowingaresomeexamplesofInvisibleChallenges.NotethatthislistisprovidedsolelyforillustrativepurposesanddoesnotrepresentthefullrangeofInvisibleChallengesthatmaybeemployed.

RotationofMovement

Challenge:Introduceadeviationinthemousemovement.Theexamplebelow(leftimage)showsauserreactingtotheInvisibleChallengebymakingasmallcorrectiontoaright-sidedeviationthatwouldhavemadehimmisshistargetwithoutcompensating.Whengiventhischallengerepeatedly,thisusertypicallymakesonesmallcorrectionata60-80degree(redhook)madeduringthelast10%ofthemovement.Butotherpeopleresponddifferentlytothesamechallenge.Inthemiddleimage,aQAmanagerrespondswithmultiplecorrections(bluelines).Shebeginshercorrectionduringthelast20%ofthemovement.Bothusersreportedthattheydidnotsensethechallengeornoticeanythingdifferentintheuserexperience.Arobot(rightimage)wouldnotneedtocompensateatall,becausesuchmovementdoesnotinvolvehand-eyecoordination.

User1User2Robot

ThisexampledemonstratesaniPadtouchinterfacechallenge-responsebyleveragingadrag-and-dropeffect,withoutanychangetotheuserexperience.Additionalchallengescaninvolvescrolling,swiping,typingandpinching/zooming.

InvisibleChallengesFacts

• Proactiveandpassive• Injectedatspecificpoints

withinasession• Changeeachtimeina

randomizedway• Elicituniquebehavioral

andcognitiveparameters• Donotaltertheuser

experience

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SpinningWheel

Challenge:Introduceafluctuationinthewaytheselectionwheelspins.Acommonuserinteractionelementinmobileappsisthespinningselectionwheelfordates,time,numbers,etc.Thisisoftenusedwhenenteringinformationsuchasanewdestinationaccountformoneytransactions.BioCatchcollectspassivemeasuresrelatedtospinningthewheel(speed,stoppingstrategy,correctionstowardstheend),butalsointroducessubtlefluctuationsthathelpusseehowtheusersubconsciouslyreacts.

User1 User2

User1:Thechallengeisinjected,andthewheelspinsslowly(notkinetically).Theusercompensatesbyafewlongandcontinuous"pushes"tospinthewheel,andaddstwopowerfulstrokesintheotherdirectionforfine-tuningandfinaltargeting.User2:Thechallengeisinjected,andthewheelspinsslowly(notkinetically).Theusercompensatesbymanysmallandshort"pushes"tospinthewheel.Afterwards,theuseraddsseveralshort,concentratedandpowerfulstrokesinthesamedirectionforfinaltargeting.

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DisappearingMouse

Challenge:Hidethecursor.Userssearchforthecursor/mouseinverydifferentanduniqueways.Someusewidesearchpatterns,othersusesmallones,somearehorizontalwhileothersarediagonal,andcertainusersalwayssearchcounter-clockwise.Sometimesusersmoveonacertainlearningcurveandtheirresponsesvaryaccordingtotheirlocationonthecurve.Allthesecanbecapturedasuniqueparameters,however,typicallythisisnotpractical,becausethetimerequiredfortheusertoprovideenoughrelevantmousemovementstoaccuratelyauthenticatethemselvesistoolong.InvisibleChallengesunconsciously“forces”theusertomakevariousmousemovementsinaveryshorttime,allowingBioCatchtocaptureadequatedatafromtheuserin500milliseconds.Thismakesitusefulfordetectinganomaliesinuserbehaviorinnearreal-time.Theexamplebelowshows25users,eachwithaslightlydifferentsearchpatternfora

missingcursor.

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InvisibleChallenges,InvincibleChallenges

Asaclassoftechnologies,behavioralbiometricsoffersdistinctadvantagesoverotherauthenticationmodalities.Itispassive,seamless,worksinthebackgroundanddoesnotrequireactiveenrollment.Ontheotherhand,allthesecharacteristicsalsomakeshighaccuracyhardtoobtain.Intheworldofonlinetransactions,itiscriticaltokeepfalsepositivesanduserfrictiontoanabsoluteminimum,whileensuringveryaccuratefraudalerts.Invisiblechallengesmakethispossible.InvisibleChallengeshelpdeliverthepromiseofbehavioralbiometricsforcontinuousauthenticationandovercomemanyofthechallengesthattraditionalbehavioralandfraudpreventionapproachesdonotaddress:

• Accuracy:Invisiblechallengesgeneratemoredata,whichcannotbecapturedinotherways.ThedatacapturedviaInvisibleChallengesisintimateinthesensethatitdivulgescognitiveandphysiologicalparameters.Intheworldofmachinelearninganddeeplearning,theamountofdataandthequalityofdataiswhatdeterminesaccuracy.InvisibleChallengesnotonlyspeedupthedatacollectionprocess,buttheoveralldetectionandfalsepositiverates.

• RATanddevicespoofingdetection:InvisibleChallengescandetectanunnaturalresponseordelayindicatingaremoteconnectionorVirtualMachineattack;forexample,iftherearetworesponsestoasinglechallenge,thiscanbeindicativeofaRemoteAccessTrojanorManintheBrowserattack.WithBioCatch,thismethodofdetectioncanbedonewithoutanyactiveenrollmentorindexingofthemalicioustool,atanEqualErrorRate(EER)of0%.

• Roboticdetection:Traditionalbotdetectioninvolvesdevicefingerprinting,IP

addressverification,useranalytics,andendupbeingacatandmousegamethatrequireslearningthebehaviorofbotsandclassifyingthemasharmfulornot.InvisibleChallengescircumventsallthisbyrequiringtheusertocompensatesubconsciouslyviahand-eyecoordination.Giventhatbotsareautomatedtools,bynaturetheyignorethechallenges.

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• Malwaredetection:Traditionalbehavioralapproachestomalwaredetectionsimulateshumaninteractionandcomparesittothemalwareinteractionforagivenactivity.Thisisproblematicbecauseitrequiresthesystemto“know”themalwareandthelearningphasetakessometime.ByusingInvisibleChallengesinarandomizedway,themalwarewillnotknowhowandwhentorespond,anditisthereforenotnecessarytomaintainmalwarelibrarieswhichareinevitablyobsoletethemomenttheyareupdated.Thismethodhashadperfectsuccesstodate.

• Replayattacks:Traditionalbehavioralapproachesrecognizereplayattacksby

comparingthebehaviorinagivensessionagainstthebehaviorinapriorsession.Thisisnotidealbecausereplayattackscontainnatural“noise”whichinvariablymakesthemdifferentfromprevioussessionsbutstillsimilarenoughtobemarkedasvalid.InvisibleChallengesarerandomintiming,intensityandflavor,sonopastactivitycanbeusedtoproducealegitimateresponsetothechallenge,makingBioCatchimmunetoreplayattacks.Seefigurebelow.

• Risk-basedauthentication:Bydefinition,risk-basedauthenticationisamethodofapplyingvaryinglevelsofstringencytotheauthenticationprocessesbasedontheriskprofileofthepersonorthesensitivityoftheapplicationbeingaccessed.BecauseInvisibleChallengesarecompletelytransparenttotheuser,theycanbeintroducedatdifferentjunctures,andindifferentflavors,toincreasetheaccuracyofthedetectionrate.Thismakesiteasytoestablishdifferentbusinessruleswithinanapplication,sothathigherriskactivities,likeaddinganewpayee,changingthe

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phonenumberfortheaccount,makinglargetransfers,etc.canhavespecificchallengesassignedtotheminarandommanner,whilekeepingfrictionandfalsepositiveslow.

ResultsandConclusion–LessFriction.LessFraud.

Asstatedearlier,maintainingthebalanceofidentifyingrealfraudwhilemaintaininglowfalsealarmratesandlowuserfrictionisthecatch-22forbehavioralbiometricswhicharepassiveinnatureanddonotrequireanactiveenrollment.BioCatchInvisibleChallengesoptimizethisbalance.IntroducingasinglechallengeintoasessioncanlowertheEERofanyby3%;addingmorechallengesdrivesperformanceexponentially1.CriticaltothisisthetiminginwhichtheInvisibleChallengesareinjected.Usingadvanceddatascienceandmachinelearningmethods,thechallengesareintroducedasaformofrisk-basedauthenticationpriortocrucialonlinetaskssuchas:changingpayees,transferringlargesumsofmoney,updatingpersonaldetails,cardactivationanddeactivation.Moreover,challengesmayalsobeinjectedwhenthesystemrequiresmorebehavioraldatatocalculateamoredeterministicriskscore.Thisapproachensuresveryhighdetectionrateswithextremelylow-ratesoffalsepositivesbydefinition,anddifferentiatesBioCatchfromotherbehaviorialbiometricsapproaches,deliveringimmediateresultsandreturnoninvestment,withoutbeinghostagetothecat-and-mousegameoftraditionalfraudpreventionapproaches.

1Thesefiguresarebasedonrealdatacomingfromthe2milliontransactionspermonththataremonitoredby

theBioCatchsystem,togetherwithnumeroussimulatedtransactionexperiments.

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AboutBioCatchTMBioCatchisacybersecuritycompanythatdeliversbehavioralbiometrics,analyzinghuman-deviceinteractionstoprotectusersanddata.BanksandotherenterprisesuseBioCatchtosignificantlyreduceonlinefraudandprotectagainstavarietyofcyberthreats,withoutcompromisingtheuserexperience.Withanunparalleledpatentportfolioanddeploymentsatmajorbanksaroundtheworldthatcovertensofmillionsofuserstodate,BioCatchhasestablisheditselfastheindustryleader.Thecompanywasfoundedin2011byexpertsinneuralscienceresearch,machinelearningandcybersecurityandiscurrentlydeployedinleadingbanksande-commercewebsitesacrossNorthAmerica,LatinAmericaandEurope.Formoreinformation,pleasevisit:www.biocatch.com

ContactUs

www.biocatch.com

[email protected]

@biocatch

www.linkedin.com/company/biocatch

BioCatchandInvisibleChallengesaretrademarksofBioCatchLtd.ThisreportreferstoBioCatch'sregisteredpatents:US9069942,US9418221,US9450971,US9477826,US9483292,US9531733,US9531701,US9547766,US9558339.Copyright2017.Allrightsreserved.

global trends in online fraud (2016) · 2017. 10. 6. · accuracy and being able to deal with...

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