Lightweight Real-Time Detection of DDoS Attacks on Iot Devices via Power Side-Channel Analysis
Qingyu Zeng, Yunpei Xiao, Mingyu Yang, Yuko Hara–Azumi · 2025
The proliferation of Internet of Things (IoT) devices has transformed numerous domains, such as smart homes and industrial automation, but has also introduced security vulnerabilities, notably to Distributed Denial of Service (DDoS) attacks. These attacks can severely disrupt IoT functionality by overwhelming devices with excessive traffic. In this study, we propose a novel, lightweight, real-time method to detect DDoS attacks through power side-channel analysis. This nonintrusive approach analyzes power consumption patterns to identify anomalous behaviors indicative of attacks. Our approach involves preprocessing the power consumption data collected in fixed window intervals, from which we extract pivotal features to be processed by a lightweight machine learning technique. Leveraging multi-threaded processing coupled with optimizations from the Open Neural Network Exchange runtime, the system ensures scalability across diverse IoT platforms. Experimental results on an actual microcontroller demonstrate that our system achieves 98.83 % accuracy in detecting DDoS attacks with a processing latency of 25.54 ms and memory usage of 14.92 KB. Additionally, the system maintains a low false positive rate ($\mathbf{1. 6 1 \%}$at the minimum) across various detection tasks, validating the effectiveness of utilizing power side-channel signals as a robust and practical defense within secure IoT architectures.