AJDet: Lightweight Self-Adaptive Jamming Detection for IoT Networks

Wireless IoT networks are becoming increasingly important as they support a diverse range of applications. However, due to their resource constraints and limited security capabilities, these networks are particularly susceptible to attacks. Among these, jamming attacks pose a significant threat by degrading packet delivery, disrupting communications, and depleting the limited networking and energy resources of IoT systems. Hence, effective jamming detection is essential for safeguarding wireless IoT networks. However, dynamic operating environments and diverse application scenarios make it challenging to design a robust jamming detection system. To address the challenge, this paper introduces AJDet, a lightweight, online, Adaptive Jamming attack Detection system. AJDet leverages an online adaptation approach that enables the detection of both proactive and reactive jamming attacks without relying on a deployment-specific configuration. Our experimental results demonstrate that AJDet achieves over 96.5% detection accuracy without the need for manual intervention after deployment. Moreover, the system exhibits an efficient memory footprint, making it well-suited for resource-constrained devices.