Scalable, Behavior-Based Malware Clustering

Anti-malware companies receive thousands of malware samples every day. To process this large quantity, a number of automated analysis tools were developed. These tools execute a malicious program in a controlled environment and produce reports that summarize the program’s actions. Of course, the problem of analyzing the reports still remains. Recently, researchers have started to explore automated clustering techniques that help to identify samples that exhibit similar behavior. This allows an analyst to discard reports of samples that have been seen before, while focusing on novel, interesting threats. Unfortunately, previous techniques do not scale well and frequently fail to generalize the observed activity well enough to recognize related malware. In this paper, we propose a scalable clustering approach to identify and group malware samples that exhibit similar behavior. For this, we first perform dynamic analysis to obtain the execution traces of malware programs. These execut...

• Type: Conference
• Year: 2009
• Where: NDSS
• Authors: Ulrich Bayer, Paolo Milani Comparetti, Clemens Hlauschek, Christopher Krügel, Engin Kirda
• Keywords: Computer Networks, Malware, Malware Programs, Malware Samples, NDSS 2009