LOW-LEVEL BEHAVIORAL MALWARE IDENTIFICATION FOR WINDOWS OPERATING SYSTEMS

Аннотация

We provide an e10lanation of a minimal behavioral malware detection method that makes use of Microsoft Windows prefetch files. We show that our malware detection scales linearly for training samples and achieves a high detection rate with a low false-positive rate of 1×10-3. We test our malware detection's generalizability on two distinct Windows platforms using two different sets of applications. We examine the decline in our malware detection system's performance due to concept drift and its capacity for adaptation. Lastly, we demonstrate an efficient auxiliary defensive method against such attacks and compare our malware detection performance against evasive malware.