Publication Date
Spring 2013
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
Abstract
A masquerader is an attacker who gains access to a legitimate user’s credentials and pretends to be that user so as to avoid detection. Several statistical techniques have been applied to the masquerade detection problem, including hidden Markov models (HMM) and one class na ̈ Bayes (OCNB). In addition, Kullback-Leibler ıve (KL) divergence has been used in an effort to improve detection rates. In this project, we develop and analyze masquerade detection techniques that employ KL divergence, HMMs, and ONCB. Detailed statistical analysis is provided to show that our results outperform previous related research.
Recommended Citation
Viswanathan, Geetha Ranjini, "Analysis of Kullback-Leibler Divergence for Masquerade Detection" (2013). Master's Projects. 302.
DOI: https://doi.org/10.31979/etd.5c9n-a6mc
https://scholarworks.sjsu.edu/etd_projects/302
