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Efficient and interpretable real-time malware detection using random-forest

Mills, Alan; Spyridopoulos, Theodoros; Legg, Phil

Authors

Alan Mills alan2.mills@live.uwe.ac.uk

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Dr Phil Legg Phil.Legg@uwe.ac.uk
Associate Professor in Cyber Security



Abstract

© 2019 IEEE. Malicious software, often described as malware, is one of the greatest threats to modern computer systems, and attackers continue to develop more sophisticated methods to access and compromise data and resources. Machine learning methods have potential to improve malware detection both in terms of accuracy and detection runtime, and is an active area within academic research and commercial development. Whilst the majority of research focused on improving accuracy and runtime of these systems, to date there has been little focus on the interpretability of detection results. In this paper, we propose a lightweight malware detection system called NODENS that can be deployed on affordable hardware such as a Raspberry Pi. Crucially, NODENS provides transparency of output results so that an end-user can begin to examine why the classifier believes a software sample to be either malicious or benign. Using an efficient Random-Forest approach, our system provides interpretability whilst not sacrificing accuracy or detection runtime, with an average detection speed of between 3-8 seconds, allowing for early remedial action to be taken before damage is caused.

Start Date Jun 3, 2019
Publication Date Jun 1, 2019
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
ISBN 9781728102320
APA6 Citation Mills, A., Spyridopoulos, T., & Legg, P. (2019). Efficient and interpretable real-time malware detection using random-forest. https://doi.org/10.1109/CyberSA.2019.8899533
DOI https://doi.org/10.1109/CyberSA.2019.8899533
Publisher URL https://doi.org/10.1109/CyberSA.2019.8899533

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