Infections as Abstract Symbolic Finite Automata: Formal Model and Applications.

In this paper, we propose a methodology, based on machine learning, for building a symbolic finite state automata based model of infected systems, that expresses the interaction between the malware and the environment by combining in the same model the code and the semantics of a system and allowing to tune both the system and the malware code observation. Moreover, we show that this methodology may have several applications in the context of malware detection.