Active malware analysis using stochastic games
Simon Williamson, Pradeep Reddy Varakantham, Ong Chen Hui, Debin Gao · 2012
Cyber security is increasingly important for defending computer systems from loss of privacy or unauthorised use. One important aspect is threat analysis — how does an attacker infiltrate a system and what do they want once they are inside. This paper considers the problem of Active Malware Analysis, where we learn about the human or software intruder by actively interacting with it with the goal of learning about its behaviours and intentions, whilst at the same time that intruder may be trying to avoid detection or showing those behaviours and intentions. This game-theoretic active learning is then used to obtain a behavioural clustering of malware, an important contribution for both understanding malware at a high level and more crucially, for the deployment of effective anti-malware defences. This paper