SIMBIoTA++: Improved Similarity-based IoT Malware Detection

The Internet of Things is quickly developing and it enables exciting new applications, but at the same time, it also brings new security risks. In particular, embedded IoT devices may be subject to malware infection, undermining the trustworthiness of IoT systems. Malware detection on IoT devices is challenging due to their resource constraints, and antivirus tools developed for desktop PCs and servers are not directly applicable for them. In an earlier paper, we proposed a lightweight antivirus solution for IoT devices, called SIMBIoTA. In this paper, we propose SIMBIoTA++, an improvement on SIMBIoTA in terms of resource requirements. We also present a graph theory and measurement-based argument for selecting an appropriate similarity threshold, which is a key parameter in both SIMBIoTA and SIMBIoTA++.