Malware Classification with GMM-HMM Models

Jing Zhao; Samanvitha Basole; Mark Stamp

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arXiv · 2103.02753

Malware Classification with GMM-HMM Models

Jing Zhao·Samanvitha Basole·Mark Stamp

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Abstract

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.

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BibTeX RIS

Jing Zhao, Samanvitha Basole, Mark Stamp. 2021-03-03. Malware Classification with GMM-HMM Models. https://arxiv.org/abs/2103.02753

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