Towards Obfuscated Malware Detection for Low Powered IoT Devices
This addresses the challenge of protecting resource-constrained IoT devices from malware threats, though it is incremental as it builds on existing feature extraction techniques.
The paper tackled the problem of detecting obfuscated malware on low-powered IoT devices by proposing a method using Markov matrices from opcode traces, achieving a high detection rate with lower power consumption compared to similar approaches.
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.