Malware Detection Using Dynamic Birthmarks
Swapna Vemparala, Fabio Di Troia, Visaggio Aaron Corrado, Thomas H. Austin, Mark Stamo · 2016
In this paper, we compare the effectiveness of Hidden Markov Models (HMMs) with that of Profile Hidden Markov Models (PHMMs), where both are trained on sequences of API calls. We compare our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in comparing our two dynamic analysis approaches, we find that using PHMMs consistently outperforms our technique based on HMMs.