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.
Random Forest for Malware Classification
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abstract
The challenge in engaging malware activities involves the correct identification and classification of different malware variants. Various malwares incorporate code obfuscation methods that alters their code signatures effectively countering antimalware detection techniques utilizing static methods and signature database. In this study, we utilized an approach of converting a malware binary into an image and use Random Forest to classify various malware families. The resulting accuracy of 0.9562 exhibits the effectivess of the method in detecting malware
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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.