Machine Learning Anomaly Detection using Binary Visualisation
Stavros N. Shiaeles · 2021
Internet of Things devices have seen a rapid growth in recent years. The ubiquity of IoT devices with the limitation of resources, it is becoming increasingly harder to protect against security threats such as malware due to the fact that they are evolving faster than the defence mechanisms. The traditional security systems are not able to detect unknown malware as they use signature-based methods. In this work we aim to address this issue by introducing a novel IoT malware traffic analysis approach using machine learning and binary visualisation. The work was supported by European Union under the CYBER-TRUST project https://cyber-trust.eu/ which aims to develop an innovative cyber-threat intelligence gathering, detection, and mitigation platform, as well as, to perform high quality interdisciplinary research in key areas for introducing novel concepts and approaches to tackle the grand challenges towards securing the ecosystem of IoT devices.