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.
Le, Q., Boydell, O., Namee, B. M., & Scanlon, M. (2018). Deep learning at the shallow end: Malware classification for non-domain experts. In Proceedings of the Digital Forensic Research Conference, DFRWS 2018 USA (pp. S118–S126). Digital Forensic Research Workshop. https://doi.org/10.1016/j.diin.2018.04.024