Stamad

Abstract

One of the main challenges in malware detection is the discovery of malicious behaviors. This task requires a huge amount of engineering and manual study of the code. To avoid this tedious manual task, we propose in this paper a tool, called STAMAD, that, given a training set of known malwares and benign programs, (1) either automatically extracts malicious behaviors using Information Retrieval techniques, or (2) applies machine learning techniques to automatically learn malwares. Then, in both cases, STAMAD can classify a new given unseen program as malicious or benign.