1. Keystroke Dynamics Etem DENİZ, Buğra KOCATÜRK, Gülşah YILDIZOĞLU, Ömer UZUN Boğaziçi University, CMPE, May 2010

2. Agenda Introduction Data Sets / Techniques Algorithms Performance Conclusions Q & A

3. Introduction Keystroke, a developing biometric technique for user authentication Keystroke, a developing biometric technique for user authentication Secure system because different typing rhythms Secure system because different typing rhythms Keystroke terms, latency, keystroke dynamics, click patterns Keystroke terms, latency, keystroke dynamics, click patterns Only using keystroke dynamics loses effectiveness Only using keystroke dynamics loses effectiveness Neural networks, k-nearest neighbor algorithm, Manhattan(scaled), pattern classification methods to train system Neural networks, k-nearest neighbor algorithm, Manhattan(scaled), pattern classification methods to train system

4. Data Sets / Techniques t e s t t₁ t₂ t₃ t₄ t₅t₆t₇ t₈ hold time up-down latency down-down latency H.t = t ₂ - t ₁ UD.t.e = t ₃ - t ₂ DD.t.e = t ₃ - t ₁

5. Algorithms Manhattan (scaled) Nearest Neighbor Neural Network (standard)

6. Manhattan (scaled)

7. Performance – Benchmark Data Set The data consist of keystroke-timing information from 51 subjects (typists), each typing a password (.tie5Roanl) 400 times. Test all Users:  GAR = 0.9084, FAR: 0.2341 Test single User:  User: ‘s002’, GAR: 0.9025, FAR: 0.0372  User: ‘s017’, GAR: 0.9275, FAR: 0.0260  User: ‘s053’, GAR: 0.9550, FAR: 0.1782

8. Nearest Neighbor Training Phase  Mean vectors of all user’s entries are constructed Test Phase  Euclidean Distance between test entry and the constructed mean vectors are calculated  The one with the minimum value is selected as the current user

9. Experimental Results The results showed that implementation of Nearest Neighbor is not very effective So, just use of this methodology is not reliable. Combination of both techniques are used to increase the reliability.

10. Live Demo

11. Performance of Our Application After testing our system  Password authentication: GAR : Very high FAR = Very low To calculate exact values, we need complex password and large data set.  Password + constant text Increases reliability

12. Conclusions Keystroke biometrics user authentication system is based on a password and keystroke biometric features Keystroke biometrics user authentication system is based on a password and keystroke biometric features The system offers a higher level of security and convenience for computers The system offers a higher level of security and convenience for computers For our project we have selected Neural Networks, Manhattan (scaled), and algorithms and “latency between consecutive keystrokes” and/or “duration of the keystroke, hold-time” t echniques. For our project we have selected Neural Networks (standard), Manhattan (scaled), and Nearest Neighbor (Mahalanobis) algorithms and “latency between consecutive keystrokes” and/or “duration of the keystroke, hold-time” t echniques.

13. Conclusions (cont’d) To train system Manhattan (scaled) and Nearest Neighbor algorithms are used successfully, but neural networks (standard) can not. It is because that training process takes too long, and returning value is not suitable to test system.