Converting HMM hidden-state sequences into images and classifying them with a CNN yields 0.9781 accuracy on a 7-family Malicia subset, a 0.0023 gain over the authors' HMM-RF baseline.
Malware Classification with GMM-HMM Models
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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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Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network
Converting HMM hidden-state sequences into images and classifying them with a CNN yields 0.9781 accuracy on a 7-family Malicia subset, a 0.0023 gain over the authors' HMM-RF baseline.