Contribution to Book
Deep learning versus gist descriptors for image-based malware classification
Proceedings of the 4th International Conference on Information Systems Security and Privacy
(2018)
Abstract
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.
Disciplines
Publication Date
January 22, 2018
ISBN
978-989-758-282-0
Citation Information
Sravani Yajamanam, Vikash Raja Samuel Selvin, Fabio Di Troia and Mark Stamp. "Deep learning versus gist descriptors for image-based malware classification" Proceedings of the 4th International Conference on Information Systems Security and Privacy (2018) Available at: http://works.bepress.com/mark_stamp/56/