Authors: Le, Quan; Boydell, Oisín; Mac Namee, Brian and Scanlon, Mark

Publication Date: July 2018

Publication Name: Digital Investigation

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.

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BibTeX Entry:

@article{le2018deeplearningmalware,
author="Le, Quan and Boydell, Oisín and Mac Namee, Brian and Scanlon, Mark",
title="Deep Learning at the Shallow End: Malware Classification for Non-Domain Experts",
booktitle="Digital Investigation",
volume = "26",
year="2018",
month="07",
pages = "S118 - S126",
publisher="Elsevier",
doi = "https://doi.org/10.1016/j.diin.2018.04.024",
url = "http://www.sciencedirect.com/science/article/pii/S1742287618302032",
keywords = "Deep learning, Machine learning, Malware analysis, Reverse engineering",
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."
}

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