Deriving common malware behavior through graph clustering

  • Authors:
  • Younghee Park;Douglas Reeves

  • Affiliations:
  • N.C. State University, Raleigh, NC;N.C. State University, Raleigh, NC

  • Venue:
  • Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security
  • Year:
  • 2011

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Abstract

Detection of malicious software (malware) continues to be a problem as hackers devise new ways to evade available methods. The proliferation of malware and malware variants requires methods that are both powerful, and fast to execute. This paper proposes a method to derive the common execution behavior of a family of malware instances. For each instance, a graph is constructed that represents kernel objects and their attributes, based on system call traces. The method combines these graphs to develop a supergraph for the family. This supergraph contains a subgraph, called the HotPath, which is observed during the execution of all the malware instances. The proposed method is scalable, identifies previously-unseen malware instances, shows high malware detection rates, and false positive rates close to 0%.