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A Comprehensive Approach for Malicious Javascript Detection
Friday, Sep 18, 2009
4:00-5:00pm, 140 SH (Schaeffer Hall)
Abstract
As the World Wide Web expands and more users join, it becomes an increasingly
attractive means of distributing malware. Malicious javascript frequently serves
as the initial infection vector for malware. We train several classifiers to
detect malicious javascript and evaluate their performance. We propose features
focused on detecting obfuscation, a common technique to bypass traditional
malware detectors. As the classifiers show a high detection rate and a low
false alarm rate, we propose several uses for the classifiers, including
selectively suppressing potentially malicious javascript based on the
classifier's recommendations, achieving a compromise between usability and
security. This work is jointly done with Peter Likarish and Insoon Jo, and
will appear in the proceedings of the 4th International Malicious and Unwanted
Software (Malware 2009).
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