ICISSP 2021 - Proceedings of the 7th International Conference on Information Systems Security and Privacy
Discrete hidden Markov models (HMM) are often applied to malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture model-HMMs (GMM-HMM), are rarely considered in the field of cybersecurity. In this paper, we use GMM-HMMs for malware classification and we compare our results to those obtained using discrete HMMs. As features, we consider opcode sequences and entropy-based sequences. For our opcode features, GMM-HMMs produce results that are comparable to those obtained using discrete HMMs, whereas for our entropy-based features, GMM-HMMs generally improve significantly on the classification results that we have achieved with discrete HMMs.
Gaussian Mixture Model, GMM-HMM, Hidden Markov Model, HMM, Malware
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Jing Zhao, Samanvitha Basole, and Mark Stamp. "Malware classification with GMM-HMM models" ICISSP 2021 - Proceedings of the 7th International Conference on Information Systems Security and Privacy (2021): 753-762. https://doi.org/10.5220/0010409907530762