PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things
Auwal Sani Iliyasu, Abdul Jabbar Siddiqui, Houbing Song, Fahad Jibrin Abdu
Abstract
Network Intrusion Detection Systems (NIDS) play a crucial role in IoT security. In recent years, deep learning-based intrusion detection systems have demonstrated excellent performance. However, the high computational and storage requirements make these impractical for most IoT devices. To address this pressing issue, we propose PNet-IDS, a novel lightweight convolutional neural network (CNN)-based method to reduce computational complexity and optimize on-device resource usage for real-time intrusion detection. The key contribution of the proposed method is the reduced number of floating point operations (FLOPs) and effective utilization of on-device computational resources at high accuracies and precision, making PNet-IDS lightweight and efficient for real-time next generation IoT intrusion detection. Moreover, PNet-IDS’ robustness against distribution shifts in network traffic is enhanced by through a knowledge distillation framework. Comprehensive experimental evaluations using the popular BoT-IoT and CIC-IDS2017 benchmark datasets prove the superiority of the proposed PNet-IDS over competitive related methods in terms of reduced parameters count, reduced FLOPs, reduced model size while maintaining high accuracy and precision. By combining PNet-IDS’ efficiency with knowledge distillation’s adaptability, the proposed method offers a scalable and resilient solution for IoT intrusion detection.