Malware Classification with GMM-HMM Models

• URL: http://arxiv.org/abs/2103.02753v1
• Date: Wed, 3 Mar 2021 23:23:48 GMT
• Title: Malware Classification with GMM-HMM Models
• Authors: Jing Zhao and Samanvitha Basole and Mark Stamp
• Abstract summary: In this paper, we use GMM-HMMs for malware classification and we compare our results to those obtained using discrete HMMs. For our opcode features, GMM-HMMs produce results that are comparable to those obtained using discrete HMMs.
• Score: 8.02151721194722
• License: http://creativecommons.org/licenses/by/4.0/
• 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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