With the proliferation of Internet of Things (IoT) devices, IoT security has become an increasingly important issue. In particular, robust malware detection strategies for IoT devices are in high demand. However, the constraints imposed by the limited memory and processing power of IoT devices make IoT malware detection a challenging problem. Existing solutions primarily use either local computation or cloud-based services, each with its own set of challenges. In this work, we (me and my co-workers) proposed a hybrid malware detection method that leverages both local and cloud-based computation to address these challenges. Through experimental evaluation, we demonstrated that our proposed approach achieves an optimal balance between local and cloud-based computations, providing a promising solution for malware detection in the IoT context.
Dr. Buttyán Levente
2024-01-14
Támogató: PARIPA (Cujo AI)