Deep learning at the shallow end: Malware classification for non-domain experts. (July 2018)

• Record Type: Journal Article
• Authors: Le, Quan; Boydell, Oisín; Mac Namee, Brian; Scanlon, Mark
• Is Part Of: Digital investigation. Volume 26(2018)Supplement
• Page Start: S118
• Page End: S126
• Publication Date: 2018-07
• Subjects: Deep learning -- Machine learning -- Malware analysis -- Reverse engineering; Forensic sciences -- Data processing -- Periodicals; Criminal investigation -- Data processing -- Periodicals; 363.250285
• DOI: 10.1016/j.diin.2018.04.024
• Languages: English
• ISSNs: 1742-2876

Abstract

Abstract: Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns ( signatures ) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.