Abstract. Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the e«ectiveness of classical signature-based detection. Yet variants of malware families share typical behavioral patterns reflecting its origin and purpose. We aim to exploit these shared patterns for classification of malware and propose a method for learning and discrimination of malware behavior. Our method proceeds in three stages: (a) behavior of collected malware is monitored in a sandbox environment, (b) based on a corpus of malware labeled by an anti-virus scanner a malware behavior classifier is trained using learning techniques and (c) discriminative features of the behavior models are ranked for explanation of classification decisions. Experiments with di«erent heterogeneous test data collected over several months using honeypots demonstrate the e«ectiveness of our method, especially in detecting novel instances of malware families previously not recognized by commercial anti-virus software.
In REF the authors propose a new method to learn and discriminate malware behaviour.
Learning and Classification of Malware Behavior
In Fifth Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA 08
Computer Science