Automatic Analysis of Malware Behavior using Machine Learning



	Machine Learning for Computer Security [http://www.mlsec.org/] Malicious software-so called malware—poses a major threat to the security of com- puter 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 form of computer viruses, Internet worms and Trojan horses. While obfuscation and poly-morphism 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 automatic analysis of malware behav- ior 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 to process 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 provid-ing an accurate discovery and discrimination of novel malware variants.