Exploring Hidden Markov Models for Virus Analysis: A Semantic Approach

Recent work has presented hidden Markov models (HMMs) as a compelling option for virus identification. However, to date little research has been done to identify the meaning of these hidden states. In this paper, we examine HMMs for four different compilers, hand-written assembly code, three virus construction kits, and a metamorphic virus in order to note similarities and differences in the hidden states of the HMMs. Furthermore, we develop the dueling HMM Strategy, which leverages our knowledge about different compilers for more precise identification. We hope that this approach will allow for the development of better virus detection tools based on HMMs.