A GAN-based Hybrid Deep Learning Approach for Enhancing Intrusion Detection in IoT Networks

Abstract

Internet of Things (IoT) strongly involves intelligent objects sharing information to achieve tasks in the environment with an excellence of living standards. In resource-constrained it is extremely difficult chore to impart security against intrusion. It is unprotected from Distributed Denial of Service (DDoS), Gray hole, sinkhole, wormhole attacks, spoofing, and Sybil attacks. Recent years, deep neural network (DNN) methodologies are widely used to detect malicious attacks. We develop a Hybrid deep learning based GAN Network to detect malicious attacks in IoT networks. Due to composite and time-varying vigorous environment of IOT networks, the model trainig samples are insufficient since intrusion samples combined with normal samples will lead to high false detection rate. We created a dynamic distributed IDS to detect malicious behaviors without centralized controllers. Preprocessing sets threshold values to identify malicious behaviors. Experimental results show HDGAN outperforms existing algorithms with higher accuracy 98%, precision 98% and 95% lower False Positive Rate (FPR).