Towards Obfuscated Malware Detection for Low Powered IoT Devices

• URL: http://arxiv.org/abs/2011.03476v1
• Date: Fri, 6 Nov 2020 17:10:26 GMT
• Title: Towards Obfuscated Malware Detection for Low Powered IoT Devices
• Authors: Daniel Park, Hannah Powers, Benji Prashker, Leland Liu and B\"ulent Yener
• Abstract summary: IoT and edge devices have become a new threat vector for malware authors. Due to their limited computational power and storage space, it is infeasible to deploy state-of-the-art malware detectors onto these systems. We propose using and extracting features from Markov matrices constructed from opcode traces as a low cost feature for unobfuscated and obfuscated malware detection.
• Score: 0.11417805445492081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increased deployment of IoT and edge devices into commercial and user networks, these devices have become a new threat vector for malware authors. It is imperative to protect these devices as they become more prevalent in commercial and personal networks. However, due to their limited computational power and storage space, especially in the case of battery-powered devices, it is infeasible to deploy state-of-the-art malware detectors onto these systems. In this work, we propose using and extracting features from Markov matrices constructed from opcode traces as a low cost feature for unobfuscated and obfuscated malware detection. We empirically show that our approach maintains a high detection rate while consuming less power than similar work.

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