Resource- and Workload-Aware Model Parallelism-Inspired Novel Malware Detection for IoT Devices

The wide adoption of Internet of Things (IoT) devices has led to better connectivity along with seamless communication and smart computation capabilities across the network. Despite being deployed widely across the globe, IoT devices are prominently exploited for security vulnerabilities due to the lack of inherent security measures. Among multiple threats, malicious applications also known as malware is a pivotal security threat for IoT devices. Lack of security traits and limited resources are the primary hindrances for the adoption of existing malware detection techniques in IoT devices. Furthermore, the existing techniques assume the availability of all the device resources for malware detection. However, for IoT devices deployed for critical real-world applications, the available on-device resources for a given task, including malware detection are minimal compared to the overall available resources. To address this primary challenge, this work introduces a novel resource- and workload-aware model-parallelism-inspired malware detection for IoT devices. The device first analyzes the available resources for malware detection using a lightweight regression model. Depending on the available resources, ongoing workload executions, and communication costs, the malware detection task is either performed on-device or offloaded to neighboring IoT nodes with sufficient resources. To ensure data integrity and user privacy, instead of offloading the whole malware detection, the classifier is partitioned and distributed over multiple nodes and further integrated at the parent node for malware detection. Experimental analysis shows that the proposed technique can achieve a speed-up of
$9.8\times $
compared to on-device inference while maintaining a malware detection accuracy of 96.7%.