Active authentication for mobile devices utilising behaviour profiling

International Journal of Information Security

Volumn 13, Issue 3, 2014, Pages 229-244

  Li, Fudong ^a   Clarke, Nathan ^a,b   Papadaki, Maria ^a   Dowland, Paul ^a  

^a UNIVERSITY OF PLYMOUTH   (United Kingdom)

^b EDITH COWAN UNIVERSITY   (Australia)

Author keywords

Active authentication; Behaviour profiling; Biometrics

Indexed keywords

BIOMETRICS; FUNCTIONS; MOBILE DEVICES; MOBILE TELECOMMUNICATION SYSTEMS;

ABNORMAL APPLICATIONS; BEHAVIOUR PROFILING; BIOMETRIC TECHNIQUES; FALSE ACCEPTANCE RATE; FALSE REJECTION RATE; PERSONAL IDENTIFICATION NUMBER; RULE-BASED CLASSIFIER; SECURITY AND USABILITIES;

AUTHENTICATION;

EID: 84901199405     PISSN: 16155262     EISSN: 16155270     Source Type: Journal    
DOI: 10.1007/s10207-013-0209-6     Document Type: Article

References (32)

1. Bishop, M.: Neural Networks for Pattern Classification. Oxford University Press, Oxford (1995).
2. Boukerche, A., Nitare, M. S. M. A.: Behavior-based intrusion detection in mobile phone systems. J. Parallel Distrib. Comput. 62(9), 1476-1490 (2002).
3. Buschkes, R., Kesdogan, D., Reichl, P.: How to increase security in mobile networks by anomaly detection. In: Proceedings of the 14th Annual Computer Security Applications Conference, pp. 3-12 (1998).
4. Clarke, N.: Transparent User Authentication. Springer, Berlin (2011).
5. Clarke, N. L., Furnell, S. M.: Authenticating mobile phone users using keystroke analysis. Int. J. Inf. Secur. 6(1), 1-14 (2006).
6. Clarke, N. L., Furnell, S. M.: Authentication of users on mobile telephones-a survey of attitudes and practices. Comput. Secur. 24(7), 519-527 (2005).
7. Clarke, N. L., Mekala, A. R.: The application of signature recognition to transparent handwriting verification for mobile devices. Inf. Manag. Comput. Secur. 15(3), 214-225 (2007).
8. Clarke, N. L., Karatzouni, S., Furnell, S. M.: Flexible and transparent user authentication for mobile devices. In: Proceedings of the 24th IFIP TC 11 International Information Security Conference, Pafos, Cyprus, May 18-20, ISBN: 978-3-642-01243-3, pp. 1-12 (2009).
9. Credant, Phone Data makes 4. 2 Million Brits Vulnerable to ID Theft, Credant, http://www. credant. com/news-a-events/press-releases/337-phone-data-makes-42-million-brits-vulnerable-to-id-theft. html (2009), Accessed: 14 June 2012.
10. DARPA, Active Authentication, DARPA, http://www. darpa. mil/OurWork/I2O/Programs/ActiveAuthentication. aspx (2011), Accessed: 17 April 2012.
11. Derawi, M. O., Nickel, C., Bours, P., Busch, C.: Unobtrusive user authentication on mobile phones using biometric gait recognition. In: Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 306-311 (2010).
12. Eagle, N., Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. In: Proceedings of the National Academy of Sciences (PNAS), vol. 106, pp. 15274-15278 (2009).
13. FBI, Smishing and Vishing, The FBI, http://www. fbi. gov/news/stories/2010/november/cyber112410/cyber112410 (2010), Accessed: 11 April 2012.
14. Flurry, Mobile Apps: Models, Money and Loyalty. Flurry Smartphone Industry Pulse, http://blog. urry. com/bid/26376/Mobile-Apps-Models-Money-and-Loyalty (2009), Accessed: 01 August 2012.
15. Gosset, P.: ASPeCT: Fraud Detection Concepts: Final Report. Doc Ref. AC095/VOD/W22/DS/P/18/1 (1998).
16. Hall, J., Barbeau, M., Kranakis, E.: Anomaly based intrusion detection using mobility profiles of public transportation users. In: Proceeding of IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, vol. 2, pp. 17-24 (2005).
17. ITU, Key Global Telecom Indicators for the World Telecommunication Service Sector, International Telecommunication Union, http://www. itu. int/ITUD/ict/statistics/atglance/KeyTelecom. html (2011), Accessed 01 April 2012.
18. Jain, A. K., Duin, R. P. W., Mao, J.: Statistical pattern recognition: a review. Pattern Anal. Mach. Intell. IEEE Trans. 22(1), 4-37 (2000). doi: 10. 1109/34. 824819.
19. Jain, A. K., Mao, J., Mohiuddin, K. M.: Artificial neural networks: a tutorial. Computer 29(3), 31-44 (1996). doi: 10. 1109/2. 485891.
20. Kaspersky Lab, European Users Mobile Behaviour and Awareness of Mobile Threats, Kaspersky Lab ZAO, http://www. kaspersky. com/news?id=207576289 (2011), Accessed: 25 May 2012.
21. Kurkovsky, S., Syta, E.: Digital natives and mobile phones: A survey of practices and attitudes about privacy and security. In: Proceedings of the IEEE International Symposium on Technology and Society (ISTAS), pp. 441-449 (2010).
22. Ledermuller, T., Clarke, N. L.: Risk assessment for mobile devices. In: Proceedings of Privacy and Security in Digital Business 8th International Conference, TrustBus, pp. 210-221 (2011).
23. Power, R.: Mobility and Security: Dazzling Opportunities, Profound Challenges, McAfee, http://www. mcafee. com/us/resources/reports/rp-cylab-mobilesecurity. pdf (2011), Accessed: 1 May 2012.
24. Samfat, D., Molva, R.: IDAMN: an intrusion detection architecture for mobile networks. IEEE J. Sel. Areas Commun. 15(7), 1373-1380 (1997).
25. Samsung., Galaxy Nexus, Samsung, http://www. samsung. com/uk/consumer/mobile-devices/smartphones/android/GT-I9250TSAXEU (2012), Accessed: 04 May 2012.
26. Securelist, Mobile Malware Evolution: An Overview, Part 3, Securelist, http://www. securelist. com/en/analysis?pubid=204792080 (2009), Accessed: 30 March 2012.
27. Shabtai, A., Kanonov, U., Elovici, Y.: Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method. J. Syst. Softw. 83(8), 1524-1537 (2010).
28. Sun, B., Chen, Z., Wang, R., Yu, F., Leung, V. C. M.: Towards adaptive anomaly detection in cellular mobile networks. In: the IEEE Consumer Communications and Networking Conference, vol. 2, pp. 666-670 (2006).
29. Sun, B., Yu, F., Wu, K., Leung, V. C. M.: Mobility-based anomaly detection in cellular mobile networks. In: Proceedings of ACM Wireless Security (WiSe 04), pp. 61-69 (2004).
30. Which?, 13. 5 million UK mobile phone users at risk of fraud, Which? Tech Daily, http://blogs. which. co. uk/mobile/mobile-phones/13-5-million-uk-mobile-phone-users-at-risk-of-fraud/ (2011), Accessed: 31 July 2012.
31. Wolpert, D. H., Macready, W. G.: No free lunch theorems for optimization. IEEE Trans. Evolut. Comput. 1, 67-82 (1997).
32. Woo, R., Park, A., Hazen. T.: The MIT mobile device speaker verification corpus: data collection and preliminary experiments. In: Speaker and Language Recognition Workshop, pp. 1-6 (2006).