Publication Date
1-1-2021
Document Type
Conference Proceeding
Publication Title
ICISSP 2021 - Proceedings of the 7th International Conference on Information Systems Security and Privacy
DOI
10.5220/0010409907530762
First Page
753
Last Page
762
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.
Keywords
Gaussian Mixture Model, GMM-HMM, Hidden Markov Model, HMM, Malware
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Computer Science
Recommended Citation
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
