Keystroke Biometric Identification and Authentication on Long-Text Input Summary of eight years of research in this area Charles Tappert Seidenberg School of CSIS, Pace University

DPS+PhD Biometric Dissertations Completed – Keystroke Biometric (long text input) Identification: feasibility study – Mary Curtin 2006 Identification: desk/laptop + copy/free text – Mary Villani 2006 Identification: touch-type feature/fallback hierarchy – Mark Ritzmann 2007 Authentication: kNN ROC curve derivation methods – Robert Zack 2010 Authentication: statistical fallback for missing/incomplete info – Steve Kim 2013 – Keystroke Biometric (short and long text input) Authentication: text/spreadsheet/browser/keypad input – Ned Bakelman 2014 – Stylometry + Keystroke Biometric (long text input) Authentication of online test-takers – John Stewart 2012 In Progress – Keystroke Biometric (short and long text input) Authentication of Impaired Users – Gonzalo Perez Authentication on Smartphones of Short Text Input – Mike Coakley Authentication System Improvements – Vinnie Monaco – Stylometry Authentication of Facebook Postings – Jenny Li – Speaker Verification Common passphrase approach: “My name is” – Jonathan Leet Qualitative study replacing username/password with biometrics – James Sicuranza?, Hugh Eng? – Mouse Movement (Phil Dressner?) – Authentication Biometrics on Handhelds (Leigh Anne Clevenger?, Alecia Copeland?, Mantie Reid?, Rich Barilla?, Stephanie Haughton?) Keystroke Biometric Studies

References 1.L. Jain, J.V. Monaco, M.J. Caokley, and C.C. Tappert, Passcode Keystroke Biometric Performance on Smartphone Touchscreens is Superior to that on Hardware Keyboards, Int. J. Research in Computer Apps. & Info. Tech., IASTER, Vol.2, Issue 4, July-August, 2014, pp Preview of Coakley’s dissertation. 2.S. Kim, S. Cha, J.V. Monaco, and C.C. Tappert, A Correlation Method for Handling Infrequent Data in Keystroke Biometric Systems, Proc. 2nd Int. Workshop Biometrics & Forensics (IWBF 2014)., Malta, Mar Summary of Kim’s dissertation. 3.J.V. Monaco, J.C. Stewart, S. Cha, and C.C. Tappert, Behavioral Biometric Verification of Student Identity in Online Course Assessment and Authentication of Authors in Literary Works, Proc. IEEE 6th Int. Conf. Biometrics, Wash. D.C., Sep Preview of Monaco’s dissertation. 4.N. Bakelman, J.V. Monaco, S. Cha, and C.C. Tappert, Keystroke Biometric Studies on Password and Numeric Keypad Input, Proc European Intelligence and Security Informatics Conf., Sweden, Aug Summary of Bakelman’s dissertation. 5.J.V. Monaco, N. Bakelman, S. Cha, and C.C. Tappert, Recent Advances in the Development of a Long-Text-Input Keystroke Biometric Authentication System for Arbitrary Text Input, Proc. European Intell. and Sec. Inform. Conf., Sweden, Aug J.V. Monaco, N. Bakelman, S. Cha, and C.C. Tappert, Developing a Keystroke Biometric System for Continual Authentication of Computer Users, Proc. European Intell. and Sec. Inform. Conf., Denmark, Aug 2012, pp J.C. Stewart, J.V. Monaco, S. Cha, and C.C. Tappert, "An Investigation of Keystroke and Stylometry Traits," Proc. Int. Joint Conf. Biometrics (IJCB 2011), Wash. D.C., Oct Summary of Stewart’s dissertation. 8.C.C. Tappert, S. Cha, M. Villani, and R.S. Zack, "A Keystroke Biometric System for Long-Text Input," Int. J. Info. Security and Privacy (IJISP), Vol 4, No 1, 2010, pp Best overall summary of keystroke system. 9.R.S. Zack, C.C. Tappert and S.-H. Cha, "Performance of a Long-Text-Input Keystroke Biometric Authentication System Using an Improved k- Nearest-Neighbor Classification Method," Proc. IEEE 4th Int Conf Biometrics: Theory, Apps, and Systems (BTAS 2010), Washington, D.C., Sep Summary of Zack’s dissertation. 10.S. Cha, Y. An, and C.C. Tappert, "ROC Curves for Multivariate Biometric Matching Models," Proc. Int. Conf. Artificial Intelligence and Pattern Recognition, Orlando, Florida, July C.C. Tappert, M. Villani, and S. Cha, "Keystroke Biometric Identification and Authentication on Long-Text Input," pp , Chapter 16 in Behavioral Biometrics for Human Identification: Intelligent Applications, Edited by Liang Wang and Xin Geng, Medical Information Science Reference, M. Villani, C.C. Tappert, G. Ngo, J. Simone, H. St. Fort, and S. Cha, "Keystroke Biometric Recognition Studies on Long-Text Input under Ideal and Application-Oriented Conditions," Proc. CVPR 2006 Workshop on Biometrics, New York, NY, June Summary of Villani’s dissertation.

Keystroke Biometric Studies Introduction Build a Case for Usefulness of Study Validate importance of study – applications Define keystroke biometric Appeal of keystroke over other biometrics Previous work on the keystroke biometric No direct study comparisons on same data Feature measurements Make case for using: data over the internet, long text input, free (arbitrary) text input Extends previous work by authors Summary of scope and methodology Summary of paper organization

Keystroke Biometric Studies Introduction Validate importance of study – applications Internet authentication application – Authenticate (verify) student test-takers Internet identification application – Identify perpetrators of inappropriate Internet security for other applications – Important as more businesses move toward e-commerce

Keystroke Biometric Studies Introduction Define Keystroke Biometric The keystroke biometric is one of the less- studied behavioral biometrics Based on the idea that typing patterns are unique to individuals and difficult to duplicate

Keystroke Biometric Studies Introduction Appeal of Keystroke Biometric Not intrusive – data captured as users type – Users type frequently for business/pleasure Inexpensive – keyboards are common – No special equipment necessary Can continue to check ID with keystrokes after initial authentication – As users continue to type

Keystroke Biometric Studies Introduction Previous Work on Keystroke Biometric One early study goes back to typewriter input Identification versus authentication – Most studies were on authentication Two commercial products on hardening passwords – Few on identification (more difficult problem) Short versus long text input – Most studies used short input – passwords, names – Few used long text input –copy or free text Other keystroke problems studies – One study detected fatigue, stress, etc. – Another detected ID change via monitoring

Keystroke Biometric Studies Introduction No Direct Study Comparisons on Same Data No comparisons on a standard data set – (desirable, available for many biometric and pattern recognition problems) Rather, researchers collect their own data Nevertheless, literature optimistic of keystroke biometric potential for security

Keystroke Biometric Studies Introduction Feature Measurements Features derived from raw data – Key press times and key release times – Each keystroke provides small amount of data Data varies from different keyboards, different conditions, and different entered texts Using long text input allows – Use of good (statistical) feature measurements – Generalization over keyboards, conditions, etc.

Keystroke Biometric Studies Introduction Make Case for Using Data over the internet – Required by applications Long text input – More and better features – Higher accuracy Free text input – Required by applications – Predefined copy texts unacceptable

Keystroke Biometric Studies Introduction Extends Previous Work by Authors Previous keystroke identification study – Ideal conditions Fixed text and Same keyboard for enrollment and testing – Less ideal conditions Free text input Different keyboards for enrollment and testing

Keystroke Biometric Studies Introduction Summary of Scope and Methodology Determine distinctiveness of keystroke patterns Two application types – Identification (1-of-n problem) – Authentication (yes/no problem) Two indep. variables (4 data quadrants) – Keyboard type – desktop versus laptop – Entry mode – copy versus free text

Keystroke Biometric Studies Keystroke Biometric System Components Raw keystroke data capture Feature extraction Classification for identification Classification for authentication

Keystroke Biometric Studies Keystroke Biometric System Raw Keystroke Data Capture

Keystroke Biometric Studies Keystroke Biometric System Raw Keystroke Data Capture

Keystroke Biometric Studies Keystroke Biometric System Feature Extraction Mostly statistical features – Averages and standard deviations Key press times Transition times between keystroke pairs – Individual keys and groups of keys – hierarchy Percentage features – Percentage use of non-letter keys – Percentage use of mouse clicks Input rates – average time/keystroke

Keystroke Biometric Studies Keystroke Biometric System Feature Extraction A two-key sequence (th) showing the two transition measures

Keystroke Biometric Studies Keystroke Biometric System Feature Extraction Hierarchy tree for the 39 duration categories

Keystroke Biometric Studies Keystroke Biometric System Feature Extraction Hierarchy tree for the 35 transition categories

Keystroke Biometric Studies Keystroke Biometric System Feature Extraction Fallback procedure for few/missing samples When the number of samples is less than a fallback threshold, take the weighted average of the key’s mean and the fallback mean

Keystroke Biometric Studies Keystroke Biometric System Feature Extraction Two preprocessing steps – Outlier removal Remove duration and transition times > threshold – Feature standardization Convert features into the range 0-1

Keystroke Biometric Studies Keystroke Biometric System Classification for Identification Nearest neighbor using Euclidean distance Compare a test sample against the training samples, and the author of the nearest training sample is identified as the author of the test sample

Keystroke Biometric Studies Keystroke Biometric System Classification for Authentication Cha’s vector-distance (dichotomy) model

Keystroke Biometric Studies Experimental and Data Collection Design Two independent variables – Keyboard type Desktop – all Dell Laptop – 90% Dell + IBM, Compaq, Apple, HP, Toshiba – Input mode Copy task – predefined text Free text input – e.g., arbitrary

Keystroke Biometric Studies Experimental and Data Collection Design

Keystroke Biometric Studies Subjects and Data Collection Subjects provided samples in at least two quadrants Five samples per quadrant per subject Summary of subject demographics AgeFemaleMaleTotal Under All

Keystroke Biometric Studies Experimental Results Identification experimental results Authentication experimental results Longitudinal study results System hierarchical model and parameters – Hierarchical fallback model – Outlier parameters – Number of enrollment samples – Input text length – Probability distributions of statistical features

Keystroke Biometric Studies Experimental Results Identification Experimental Results Identification performance under ideal conditions (same keyboard type and input mode, leave-one-out procedure)

Keystroke Biometric Studies Experimental Results Identification Experimental Results Identification performance under non-ideal conditions (train on one file, test on another)

Keystroke Biometric Studies Experimental and Data Collection Design

Keystroke Biometric Studies Experimental Results Authentication Experimental Results Authentication performance under ideal conditions (weak enrollment: train on 18 subjects and test on 18 different subjects)

Keystroke Biometric Studies Experimental Results Longitudinal Study Results Identification – 13 subjects at 2-week intervals – Average 6 arrow groups: 90% -> 85% -> 83% Authentication – 13 subjects at 2-week intervals – Average 6 arrow groups: 90% -> 87% -> 85% Identification – 8 subjects at 2-year interval – Average 6 arrow groups: 84% -> 67% Authentication – 8 subjects at 2-year interval – Average 6 arrow groups: 94% -> 92% (all above results under non-ideal conditions)

Keystroke Biometric Studies Experimental Results System hierarchical model and parameters Touch-type hierarchy tree for durations (Mark Ritzmann)

Keystroke Biometric Studies Experimental Results System hierarchical model and parameters Identification accuracy versus outlier removal passes

Keystroke Biometric Studies Experimental Results System hierarchical model and parameters Identification accuracy versus outlier removal distance (sigma)

Keystroke Biometric Studies Experimental Results System hierarchical model and parameters Identification accuracy versus enrollment samples

Keystroke Biometric Studies Experimental Results System hierarchical model and parameters Identification accuracy versus input text length

Keystroke Biometric Studies Experimental Results System hierarchical model and parameters Distributions of “u” duration times for each entry mode

Keystroke Biometric Studies Conclusions Results are important and timely as more people become involved in the applications of interest – Authenticating online test-takers – Identifying senders of inappropriate High performance (accuracy) results if – 2 or more enrollment samples/user – Users use same keyboard type

ROC Curves (Robert Zack, 2010) ROC curves from the kNN classifier with k=21: method m-kNN (left), method wm-kNN (center), and method hd-kNN (right).

FAR and FRR versus threshold Closed system, kNN classifier with k=21: FAR and FRR versus threshold for method m-kNN (left), wm-kNN (center), hd-kNN (right).

Conclusions (Robert Zack, Authentication Study, 2010) Keystroke password performance – approximately 10% EER – See extensive study by Killourhy & Maxion, 2009 – Advertised performance of commercial products is exaggerated Keystroke long-text performance – approximately 1% EER – Reasonable considering powerful statistical features Closed system better than open system performance Three ROC curve derivation methods developed for kNN procedure – All are two-parameter methods – k plus a threshold

Online Test-Taker Authentication (John Stewart, 2011) Best Keystroke Performance – 0.55% EER – Closed system of 30 students Best Previous Keystroke Performance – 1.0% EER – Closed system of 14 students (Robert Zack, 2010) Best Stylometry Performance – approximately 30.0% EER – Keystroke biometric operates at the automatic motor control level – Because stylometry operates at a higher cognitive word/syntax level, longer text passages are required for reasonable performance This hypothesis was verified on much longer texts of short novels

Keystroke Data Capture Systems Java Applet – Mary Curtin, Mary Villani, Mark Ritzmann, Robert Zack, Vinnie Monaco/Ned Bakelman (EISIC paper) Java Script (Vinnie Monaco) – John Stewart / Vinnie Monaco Fimbel Open Source Keylogger – Ned Bakelman / Vinnie Monaco Should we develop our own keylogger?

Continual Authentication of Computer Users (EISIC 2013 Conference Paper) Motivation – The technology is applicable to a wide range of government, private company, and academic applications worldwide – For example, to detect intruders, the U.S. Government wants to continually authenticate all government computer users, both military and non-military U.S. DARPA 2010 and 2012 Requests for Proposals Requirement – detect intruder within minutes Current study focuses on this fast detection application – Authentication of students taking online tests U.S. Higher Education Opportunity Act of EISIC 2013

Continual Burst Authentication Strategy Assumptions Most computer users tend to have bursts of input activity interspersed with periods of inactivity while doing other things The application is designed for typical business or government office computer usage Note: it would be interesting to determine the frequency and duration of bursts of computer input activity in typical office environments 47EISIC 2013

Continuous vs Continual Authentication with Data Capture Windows Continuous (ongoing) burst authentication Continual burst authentication with pauses 05 min10 min 1 min 1 min 1 min Burst 1Burst 2Burst 3 08 min30 min 1 min 1 min 1 min Pause Threshold Burst 1Burst 2Burst 3 Pause Threshold 48EISIC 2013

Continual Burst Strategy after Pauses Reduces Frequency of Authentications Avoids capture of excessive quantities of data Reduces need for excessive computing resources Reduces false alarm rate Still provides sufficient data for continual training of the biometric system 49EISIC 2013

Two Important Time Periods for Continual Burst Authenti cation 1.Length of the data capture window – Short enough to catch an intruder before significant harm is caused On the order of minutes – DARPA – Long enough to make an accurate detection and reduce false alarms 2.Length of the pause – Must be shorter than entry time of intruder – Long enough to reduce authentication rate Note: periods of little computer activity cause long pauses 50EISIC 2013

Possible Broader Intrusion Detection Plan Multi-biometric System Motor control level – keystroke + mouse movement Linguistic level – stylometry (char, word, syntax) Semantic level – target likely intruder commands Intruder Keystroke + Mouse Stylometry Motor Control Level Linguistic Level Semantic Level 51EISIC 2013

Three Experiments Dichotomy Model kNN Classification Leave-One-Out Procedure 52EISIC 2013

Experimental Results EER versus #Keystrokes 53EISIC 2013

Experimental Results ROC Curves at Maximum #Keystrokes 54EISIC 2013

Keystrokes per Typing Speed Average typing speed ~200 keystrokes/min Professional typing speed ~400 keystrokes/min Therefore, at average typing speed the EER versus #keystrokes graph goes from about ½ minute to 4 minutes indicating the time to detect an intruder 55EISIC 2013

Conclusions (EISIC 2013 Conference Paper) As the number of keystrokes per test sample increases, EER decreased roughly logarithmically EER increases with increase in population size Performance results of 99.6% on 14, 98.3% on 30, and 96.3% on 119 participants indicates the strong potential of this approach 56EISIC 2013