Malicious software -so called malware -poses a major threat to the security of computer systems. The amount and diversity of its variants render classic security defenses ineffective, such that millions of hosts in the Internet are infected with malware in the form of computer viruses, Internet worms and Trojan horses. While obfuscation and polymorphism employed by malware largely impede detection at file level, the dynamic analysis of malware binaries during run-time provides an instrument for characterizing and defending against the threat of malicious software.In this article, we propose a framework for the automatic analysis of malware behavior using machine learning. The framework allows for automatically identifying novel classes of malware with similar behavior (clustering) and assigning unknown malware to these discovered classes (classification). Based on both, clustering and classification, we propose an incremental approach for behavior-based analysis, capable of processing the behavior of thousands of malware binaries on a daily basis. The incremental analysis significantly reduces the run-time overhead of current analysis methods, while providing accurate discovery and discrimination of novel malware variants.

We study techniques to visualize the behavior of malicious software (malware). Our aim is to help human analysts to quickly assess and classify the nature of a new malware sample. Our techniques are based on a parametrized abstraction of detailed behavioral reports automatically generated by sandbox environments. We then explore two visualization techniques: treemaps and thread graphs. We argue that both techniques can effectively support a human analyst (a) in detecting maliciousness of software, and (b) in classifying malicious behavior.

No abstract

No abstract

No abstract