Privacy Theft Malware Detection with Privacy Petri Net

Privacy theft malware has become serious and challenging problem to cyber security. Previous works are based on two categories of road map, the one focuses on the outbound network traffic, the other one dives into the inside information flow. We incorporate dynamic behavior analysis with network traffic analysis and present abstract model called Privacy Petri Net (PPN) which is more applicable to various kinds of malware and more meaningful to users. We apply our approach on real world malware and the experiment result shows that our approach can effectively find categories, content, source and destination of the privacy theft behavior of the malware sample.