Towards Obfuscated Malware Detection for Low Powered IoT Devices

6 Nov 2020  ·  Daniel Park, Hannah Powers, Benji Prashker, Leland Liu, Bülent Yener ·

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. read more

PDF Abstract
No code implementations yet. Submit your code now

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here