Publication Date

Fall 2014

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Sami Khuri

Second Advisor

Thomas Austin

Third Advisor

Kunjan Kapadia

Keywords

SVD security log files intrusion detection

Abstract

Information systems and networks are highly susceptible to attacks in the form of intrusions. One such attack is by the masqueraders who impersonate legitimate users. Masqueraders can be detected in anomaly based intrusion detection by identifying the abnormalities in user behavior. This user behavior is logged in log files of different types. In our research we use the score based technique of Singular Value Decomposition to address the problem of masquerade detection on a unix based system. We have data collected in the form of sequential unix commands ran by 50 users. SVD is a linear algebraic technique, which has been previously used for applications like facial recognition. We present experimental results and we analyze the effectiveness and efficiency of this SVD-based masquerade detection.

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