On the Anatomy of the Dynamic Behavior of Polymorphic Viruses

The sophistication of novel strains of polymorphic viruses, such as Stuxnet, has increased over the last decade. Traditional tools such as anti-virus, firewalls, intrusion detection/prevention systems, etc. may be incapable of detecting such strains. As a result, new methods need to be introduced in order to detect this family of malware. Combining dynamic malware analysis techniques with machine learning tools can prove useful in the progression of developing an effective and efficient classifier. This paper explores the use of dynamic analysis of malware and machine learning to create a classifier for polymorphic virus detection.