Large-scale malware indexing using function-call graphs

A major challenge of the anti-virus (AV) industry is how to effectively process the huge influx of malware samples they receive every day. One possible solution to this problem is to quickly determine if a new malware sample is similar to any previously-seen malware program. In this paper, we design, implement and evaluate a malware database management system called SMIT (Symantec Malware Indexing Tree) that can efficiently make such determination based on malware’s function-call graphs, which is a structural representation known to be less susceptible to instruction-level obfuscations commonly employed by malware writers to evade detection of AV software. Because each malware program is represented as a graph, the problem of searching for the most similar malware program in a database to a given malware sample is cast into a nearest-neighbor search problem in a graph database. To speed up this search, we have developed an efficient method to compute graph similarity that exploit...

• Type: Conference
• Year: 2009
• Where: CCS
• Authors: Xin Hu, Tzi-cker Chiueh, Kang G. Shin
• Keywords: CCS 2009, Malware, Malware Programs, Malware Sample, Security Privacy