Deep learning versus gist descriptors for image-based malware classification

Publication Date

January 2018

Document Type

Contribution to a Book

Publication Title

Proceedings of the 4th International Conference on Information Systems Security and Privacy


Image features known as ``gist descriptors'' have recently been applied to the malware classification problem. In this research, we implement, test, and analyze a malware score based on gist descriptors, and verify that the resulting score yields very strong classification results. We also analyze the robustness of this gist-based scoring technique when applied to obfuscated malware, and we perform feature reduction to determine a minimal set of gist features. Then we compare the effectiveness of a deep learning technique to this gist-based approach. While scoring based on gist descriptors is effective, we show that our deep learning technique performs equally well. A potential advantage of the deep learning approach is that there is no need to extract the gist features when training or scoring.

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