This document provides an outline and overview of keystroke dynamics for user authentication. It discusses: - Biometrics like fingerprints and keystroke patterns that can be used for identification - Keystroke dynamics measures latency between keystrokes and duration of key presses - Literature on keystroke authentication systems, classifiers, and factors that affect performance - Approaches like static and dynamic verification, and features like hold time and up-down times - System architecture including enrollment, extraction, preprocessing, storage, and authentication - The document presents results of experiments analyzing EER, accuracy of different features, and comparisons of authentication attempts.

1. Keystroke dynamics support for
authentication mechanism
By
Mayank Sachan
12/4/2017 1
Presentation on

2. Outline
 Introduction
 Literature survey
 Types of authentication
 Keystroke Verification Techniques
 Approaches of keystroke biometric system
– Keystroke features
– Anomaly detection and accuracy measures
 Keystroke dynamics Phase
 System Architecture
 Implementation
 Experiment Result
 Conclusion
 Comments
2

3. Biometrics
Biometrics measure physical or behavioral characteristics of
an individual.
– Physical (do not change over time):
– Fingerprint, iris pattern, hand geometry
– Behavioral (may change over time):
– Signature, speech pattern, keystroke pattern
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4. Keystroke biometrics
 A keystroke dynamic is based on the assumption that each person has a
unique keystroke rhythm.
 Keystroke features are:
– Latency between keystrokes.
– Duration of key presses.
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5. Literature survey
 There have been many survey works on this topic.
 In 2004, A. Peacock et al evaluated Keystroke Dynamics system based on classifiers accuracy,
usability and privacy.[12]
 In 2010, H. Crawford found the high quality of result from different research that obtain by neural
network. This study recommend that not use same participant in authorized and unauthorized users
when evaluating system performance.[11]
 In 2012, S.P Banarjee compared different algorithms in the survey and explicitly discuss the factor that
affect the performance. [9]

6. Literature survey cont..
 In 2013, The et al. claimed that Keystroke Dynamics is unlikely to replace existing authentication
mechanism . They concluded that some properties, such as the ability to operate in stealth mode, low
cost, high user acceptance, and ease of integration with existing security systems, made KD
promising.[10]

7. Three types of authentication
By authentication we mean verifying a claimed identity.
Something you know.
• A password
 Something you have.
• An ID card or badge
 Something you are.
• Biometrics
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8. User Authentication Approaches
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Figure : 1 User Authentication Approach

9. Keystroke Verification Techniques
 Static verification (Fixed text mode)
• Only based on password typing rhythm
• Authentication only at login time
 Dynamic verification (free text mode)
• Pattern regardless of the typed text
• A continuous or periodic monitoring (On-the-fly user authentication)
• Not required to memorize a predetermined text (username & password)
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10. 12/4/2017 10
Figure 2 : Example for online exam
Currently, there are 4 primary methods for user authentication:
• Knowledge factors, or something unique that the user knows.(Username & Password)
• Ownership factors, or something unique that the user has. (ID)
• Something unique that the user is.(Photographs)
• Something unique that the user does.(Behaviour)
Continuous Biometric User Authentication (Dynamic) :

11. Keystroke dynamics features (Matrix)
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Hold Time :
Up Up Time :
Down Down Time

12. Keystroke dynamics features (Matrix) Cont..
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Down Up Time :
Up Down Time :

13. Anomaly detection techniques (Cont …)
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14. Anomaly detection techniques (Cont …)
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15. Anomaly detection techniques
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Classification accuracy measures :
FRR : False rejection rate
•FRR decreases with the higher threshold .
FAR : False acceptance rate
•FAR increases with higher threshold .
ERR : Equal error rate
FRR = FAR
•Threshold can be chosen based on EER
Figure 3 : Threshold calculation

16. Keystroke dynamics phases
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Keystroke dynamics has two phases :
Enrollment
•Capture
•Extraction
•Preprocessing extracted features
•Storing
Authentication
•Enroll
•Comparison
•Match/non-match

17. System Architecture
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Figure 4 : System architecture

18. When user inter first time , user get register
Figure 5 : UI for the data collection
Implementation

19. After entering text and re-enter text
Figure 6 : Algorithms graph

20. 12/4/2017 21
Evaluation Matrix
Figure 7 : Evaluation Matrix

21. Evaluation Table
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Figure 8 : Evaluation Table

22. Input Data
Figure 7 : Input data table

23. Result
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Figure 8 : EER of different parameter with different anomaly techniques

24. Result (Cont…)
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Figure 9 : Hold parameter for same password with different user

25. Result (Cont…)
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Figure 10 : One user attempts for the same password using different feature and classifiers

26. 12/4/2017 27
Figure 11 : Analysis for the genuine user with successful login

27. 12/4/2017 28
Figure 12 :False accepted user

28. 12/4/2017 29
Figure 12 :False rejected genuin user

29. 12/4/2017 30Figure 12 : Equal error rate for gaussian

30. Conclusion
 Got the .425 EER with Manhattan standard deviation for Down Up parameter,
Mahalanobis for Hold, Up Up, Down Up parameters and Z-score for Up Up
parameters.
 For the Gaussian classification, the Up Down and Down Up time intervals give
better results.
 In the experiment, Up Up feature gives ~70% accuracy.
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31. Some Opportunities:
 Login information
– Computer
– Cell phones
– Automated Teller Machine
– Digital telephone dial
– Digital electronic security keypad at a building entrance
 Continuous authentication
– Online examination
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32. Advantages of keystroke dynamics
 Software Only method. (No Additional Hardware except a Keyboard)
 Simple To Deploy and Use (username & passwords) – Universally accepted
 Unobtrusive, Non-Invasive, Cost Effective
 No End-User Training
 It provides a simple natural way for increased computer security
 Can be used over the internet
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33. Keystroke drawbacks
 User’s susceptibility to fatigue
 Dynamic change in typing patterns
 Injury, skill of the user
 Change of keyboard hardware.
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34. REFERENCES
[1] Alaa Darabseh, Akbar Siami Namin ,“Effective User Authentications Using Keystroke Dynamics Based on
Feature Selections ”, IEEE 14th International Conference on Machine Learning and Applications, 2015.
[2] Alaa Darabseh , Akbar Siami Namin ,“On Accuracy of Classification-based Keystroke Dynamics for
Continuous User Authentication” , International Conference on Cyberworlds, 2015 .
[3] Soumen Roy, Utpal Roy, D.D. Sinha,“Free-text User Authentication Technique through Keystroke Dynamics ”,
High Performance Computing and Applications (ICHPCA), 2014 International Conference on.
[4] Shimaa I. Hassan, Mazen M. Selim, and Hala H. zayed, “User Authentication with Adaptive Keystroke
Dynamics ”, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 4, No 2, July 2013.
[5] Fabian Monrose, Aviel D. Rubin, “Keystroke dynamics as a biometric for authentication”, Future Generation
Computer Systems 16 , March 1999.

35. [6] Daniele Gunetti, Claudia Picardi,“Keystroke Analysis of Free Text”, ACM Transactions on Information and
System Security, Vol. 8, No. 3, August 2005, Pages 312–347.
[7] Robert Moskovitch, Clint Feher, Arik Messerman, Niklas Kirschnick, Tarik Mustafić, Ahmet Camtepe,
Bernhard Löhlein, Ulrich Heister, Sebastian Möller, Lior Rokach, Yuval Elovici , “Identity theft, computers and
behavioral biometrics”, Intelligence and Security Informatics, 2009. ISI '09. IEEE International Conference on.
[8] Pahuja, G.; Nagabhushan, T.N.,” Biometric authentication & identification through behavioral biometrics: A
survey” , Cognitive Computing and Information Processing (CCIP), 2015 International Conference on.
[9]S. Banerjee and D. Woodard. “Biometric Authentication and Identification using Keystroke Dynamics: A
Survey”. In: Journal of Pattern Recognition Research 7.1 (July 2012), pp. 116–139

36. [10] P. S. Teh, A. B. J. Teoh, and S. Yue. “A Survey of Keystroke Dynamics Biometrics”. In: The Scientific World
Journal 2013 (2013), pp. 1–24. DOI: 10.1155/2013/408280
[11] H. Crawford. “Keystroke dynamics: characteristics and opportunities”. In: Proceedings of the 8th International
Conference on Privacy, Security and Trust (PST 10). Aug. 2010, pp. 205–212.
[12] A. Peacock, X. Ke, and M. Wilkerson. “Typing patterns: a key to user identification”. In: IEEE Security and
Privacy 2.5 (2004), pp. 40–47.

37. Thank You
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