Tuesday, July 2, 2013

PAPER: Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces

I've recently read another paper for the first week quiz of Coursera course 'Malicious Software and its Underground Economy: Two Sides to Every Story" and I'm happy to share it with you.

Roberto Perdiscia, Wenke Leea , and Nick Feamstera

We present a novel network-level behavioral malware clustering system. We focus on analyzing the structural similarities among malicious HTTP traffic traces generated by executing HTTP-based malware. Our work is motivated by the need to provide quality input to algorithms that automatically generate network signatures. Accordingly, we define similarity metrics among HTTP
traces and develop our system so that the resulting clusters can yield high-quality malware signatures. We implemented a proof-of-concept version of our network-level malware clustering system and performed experiments with more than 25,000 distinct malware samples. Results from our evaluation, which includes real-world deployment, confirm the effectiveness of the proposed  clustering system and show that our approach can aid the process of automatically extracting network signatures for detecting HTTP traffic generated by malware compromised machines.


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