Towards a hybrid approach for improving malware detection in IoT devices

Abstract

With the proliferation of Internet of Things (IoT) devices, cybersecurity has become an increasingly pressing concern. Due to their inherent vulnerabilities and the severity of the threats posed by malware, 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, alongside with the varied nature of these devices, have limited the development of effective malware detection strategies. Existing solutions primarily use either local computation or cloud-based services, each with its own set of challenges. In this paper, we propose a hybrid malware detection method that leverages both local and cloud-based computation to address these challenges. This method, based on similarity computations, optimizes the trade-off between computational resources and latency, offering an effective approach for malware detection in IoT devices. Through experimental evaluation using a substantial IoT malware dataset, we demonstrate that our proposed approach achieves an optimal balance between local and cloud-based computations, providing a promising solution for malware detection in IoT context.