Journal of Applied Information Science

2013 | Volume 1 | Issue Issue 1

Kullback-Leibler Divergence for Masquerade Detection

1. Geetha Ranjini Viswanathan – Department Of Computer Science, San Jose State University, San Jose, California, Usa

2. Richard M. Low – Department Of Computer Science, San Jose State University, San Jose, California, Usa

3. Mark Stamp – Department Of Computer Science, San Jose State University, San Jose, California, Usa

Received
15-Jan-2014 		Accepted
- 		Published
15-Jan-2014
Abstract
A masquerader is an attacker who gains access to a legitimate user’s credentials and pretends to be that user so as to evade detection. Several statistical techniques have been applied to the masquerade detection problem, including hidden Markov models (HMM) and one class na¨ive Bayes (OCNB). In addition, Kullback-Leibler (KL) divergence has been used in an effort to improve detection rates. In this paper, we analyze masquerade detection techniques that employ HMMs, OCNB, and KL divergence. Detailed statistical analysis is provided to compare the effectiveness of these various approaches.
Keywords Masquerade Detection, Kullback-Leibler Divergence, One Class Naive Bayes, Hidden Markov Models, Intrusion Detection
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