Abstract
Masquerading or impersonation attack refers to the act of gaining access to confidential data or greater access privileges, while pretending to be legitimate users. Detection of masquerade attacks is of great importance and is a non-trivial task of system security. Detection of these attacks is done by monitoring significant changes in user’s behavior based on his/her computer usage. Traditional detection mechanisms are based on command line system events collected using log files. In a GUI based system, most of the user activities are performed using either mouse movements and clicks or a combination of mouse movements and keystrokes. The command line data cannot capture the complete GUI event behavior of the users hence it is insufficient to detect attacks in GUI based systems. Presently, there is no frame work available to capture the GUI based user behavior in Linux systems. We are proposing a novel approach to capture the GUI based user behavior for Linux systems using our event logging tool. Our experimentation results shows that, the GUI based user behavior can be efficiently used for masquerade attack detection to achieve high detection rates with less false positives. We have applied One-class SVM on the collected data, which requires only training the user’s own legitimate sessions to build up the user’s profile. Our results on GUI data using One-class SVM gives higher detection rates with less false positives compared to a Two-class SVM approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Garg, A., Rahalkar, R., Upadhyaya, S.: Kevin Kwait Profiling Users in GUI Based Systems for Masquerade Detection. In: Proceedings of 7th Annual IEEE Information Assurance Workshop (IAW 2006), United States Military Academy, West Point, New York (June 21-23, 2006)
Heller, K.A., Svore, K.M., Keromytis, A.D., Stolfo, S.J.: One Class Vector Machines for Detecting Anomalous Windows Registry Accesses. In: Proceedings of 2003 International conference on Data Mining- (ICDM 2003) (November19, 2003)
Li, L., Manikopoulos.: Windows NT One-class Masquerade Detection. In: Proceedings of 2004 IEEE,Information Assurance Workshop (IAW 2004), United States Military Academy, West Point, New York (June 2004)
Imsand, E.S., Hamilton Jr., J.A.: GUI Usage Analysis for Masquerade Detection. In: Proceedings of 2007 IEEE, Information Assurance Workshop (IAW 2007), United States Military Academy, West Point, New York (June 21-23, 2007)
Coull, S.E., Branch, J.W., Szymanski, B.K., Breimer, E.A.: Sequence Alignment for Masquerade Detection (2006)
Coull, S., Branch, J., Szymanski, B., Breimer, E.: Intrusion detection: A bioinformatics approach. In: 19th Annual Computer Security Applications Conferences, Las Vegas, Nevada (December 8-12, 2003)
Pusara, M., Brodley, C.: User Re-authentication via mouse movements. In: Proceedings of the 2004 ACM workshop on visualization and data mining for computer security, Washington D.C., USA (October 29, 2004)
Lane, T., Brodley, C.E.: An Application of Machine Learning to Anomaly Detection. In: Proceedings of Twentieth National Information Systems Security Conference, vol. 1, (Gaithersburgh, MD), pp. 366–380. The National Institute of Standards and Technology and the National Computer Security Center (1997)
Lane, T., Brodley, C.: Sequence Matching and Learning in Anomaly Detection for Computer Security. In: Proceedings of AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management, pp. 43–49 (1997)
Schonlau, M., DuMouchel, W., Ju, W.-H., Karr, A.F., M.T., Vardi, Y.: Computer Intrusion: Detecting Masquerades. Statistical Science 16, 58–74 (2001)
Maxion, R.A., Townsend, T.N.: Masquerade Detection Using Truncated Command Lines. In: Proceedings of International Conference on Dependable Systems and Networks (DSN 2002), pp. 219–228 (2002)
Maxion, R.A.: Masquerade Detection Using Enriched Command Lines. In: Proceedings of International Conference on Dependable Systems and Networks (DSN 2003), San Francisco, CA (June 2003)
Wang, K., Stolfo, S.J.: One Class Training for Masquerade Detection. In: ICDM Workshop on Data Mining for Computer Security (DMSEC 2003) (2003)
Monrose, F., Rubin, A.: Authentication via Keystroke Dynamics. In: ACM Conference on Computer and Communications Security, pp. 48–56 (1997)
Pusara, M., Brodley, C.E.: User re-authentication via mouse movements. In: VizSEC/DMSEC 2004: Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, Washington DC, USA, pp. 1–8 (2004)
Hofmeyr, S., Forrest, S., Somayaji, A.: Intrusion Detection Using Sequences of System Calls. Journal of Computer Security 6(3), 151–180 (1998)
Forrest, S., Hofmeyr, S.A., Somayaji, A.: Computer Immunology. Communications of the ACM 40(10), 88–96 (1997)
Warrender, C., Forrest, S., Pearlmutter, B.: Detecting Intrusions using System Calls: Alternative Data Models. In: IEEE Symposium on Security and Privacy, Oakland, CA, pp. 133–145 (1999)
Wespi, A., Dacier, M., Debar, H.: Intrusion Detection Using Variable-Length Audit Trail Patterns, In Recent Advances in Intrusion Detection. In: Debar, H., Mé, L., Wu, S.F. (eds.) RAID 2000. LNCS, vol. 1907, pp. 110–129. Springer, Heidelberg (2000)
Feng, H., Kolesnikov, O., Fogla, P., Lee, W., Gong, W.: Anomaly Detection using Call Stack Information. In: Proceedings of IEEE Symposium on Security and Privacy, Oakland, California (May 2003)
Joachims, T.: Text Categorization with Support Vector Machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) Machine Learning: ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Joachims, T.: SVM light:Support Vector Machine (2004), http://www.cs.cornell.edu/People/tj/svmlight/index.html
Ghosh, A., Schwartzbard, A., Schatz, M.: Learning Program Behavior Profiles for Intrusion Detection. In: First USENIX Workshop on Intrusion Detection and Network Monitoring, pp. 51–62 (1999)
Levitt, K., Ko, C., Fink, G.: Automated Detection of Vulnerabilities in Privileged Programs by Execution Monitoring. In: Computer Security Application Conference (1994)
Schonlau, M.: Masquerading User Data (1998), http://www.schonlau.net/intrusion.html
http://developer.kde.org/documentation/books/kde-2.0-development
Dash, S.K., Reddy, K.S., Pujari, A.K.: Episode Based Masquerade Detection. In: Jajodia, S., Mazumdar, C. (eds.) ICISS 2005. LNCS, vol. 3803, pp. 251–262. Springer, Heidelberg (2005)
Kim, H.-s., Cha, S.-D.: Empherical evaluation of SVM-based masquerade detection using UNIX commands. Computers and Security 24, 160–168 (2005)
Bhukya, W.N., Kumar, S., Negi, A.: A study of effectiveness in masquerade detection IEEE TEN CON 2006 14-17, pp. 1–4 Digital Object Identifier 10.1109/TENCON.2006.344199 (November 2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bhukya, W.N., Kommuru, S.K., Negi, A. (2007). Masquerade Detection Based Upon GUI User Profiling in Linux Systems. In: Cervesato, I. (eds) Advances in Computer Science – ASIAN 2007. Computer and Network Security. ASIAN 2007. Lecture Notes in Computer Science, vol 4846. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76929-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-76929-3_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76927-9
Online ISBN: 978-3-540-76929-3
eBook Packages: Computer ScienceComputer Science (R0)