Journal
DIGITAL INVESTIGATION
Volume 26, Issue -, Pages S118-S126Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.diin.2018.04.024
Keywords
Deep learning; Machine learning; Malware analysis; Reverse engineering
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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. (C) 2018 The Author(s). Published by Elsevier Ltd on behalf of DFRWS.
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