Publication Date

Spring 2015

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Mark Stamp

Second Advisor

Sami Khuri

Third Advisor

Fabio Di Troia

Keywords

Java Malware Code Obfuscation Signature Statistical Detection

Abstract

In this research, we consider the problem of detecting malicious Java applets, based on static analysis. In general, dynamic analysis is more informative, but static analysis is more efficient, and hence more practical. Consequently, static analysis is preferred, provided we can obtain results comparable to those obtained using dynamic analysis. We conducted experiments with the machine learning technique, Hidden Markov Model (HMM). We show that in some cases a static technique can detect malicious Java applets with greater accuracy than previously published research that relied on dynamic analysis.

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