Generative Adversarial Networks for IoT Security: A Convolutional Neural Network Approach

The integration of Internet of Things (IoT) devices across various sectors has brought significant advancements but has also heightened security vulnerabilities. To address these challenges, it is essential to develop efficient and accurate detection systems for malicious network traffic at the packet level. This paper presents a novel approach utilizing a Convolutional Neural Network (CNN)-based Generative Adversarial Network (CNN-GAN) to create more robust Intrusion Detection Systems (IDSs) tailored for IoT environments. By leveraging a CNN architecture, the proposed model enhances the discriminator capacity to detect anomalies. Our extensive evaluation on a comprehensive IoT dataset reveals that the CNN-GAN achieves good performance when handling real data and synthetic data. This result underscores the CNN-GAN effectiveness in securing IoT networks against current attacks and emergent threats.