Intrusion detection and prevention system for iot systems using generative adversarial networks: challenges & solutions

The rapid growth of the Internet of Things (IoT) has brought numerous benefits to various domains, but it has also introduced new security challenges and vulnerabilities. Intrusion Detection and Prevention Systems (IDPS) play a crucial role in safeguarding IoT environments from malicious activities. This research paper presents a novel approach to anomaly detection in IoT using Generative Adversarial Networks (GANs). The proposed system leverages the power of GANs to capture normal behaviour patterns and identify anomalies in real-time. The methodology section discusses data collecting and analysing the dataset. GAN-based anomaly detection system architecture, comprising discriminator and generator networks, is shown. GAN model training and optimisation are also discussed. The research shows GAN-based system accurately detects abnormalities and typical behaviour patterns. The results of the experiments are presented, and a comparative analysis is performed with traditional IDPS methods, demonstrating the superiority of the proposed system.