A Network Intrusion Detection System Based on Enhanced CNN2D for IoT Architecture

The Internet of Things has experienced explosive evolution as a ground-breaking phenomenon since its conception. The security sector has witnessed enormous growth in cyberattacks as a consequence of the increasing growth of IoT devices, which expanded the attack vector for hackers to carry out significantly more damaging vulnerabilities. A key component of assuring the cybersecurity of IoT is the identification of anomalies in network activity using an intrusion detection system. Conventional machine learning methods appear vain in the face of inconsistent network expertise and several attack tactics. Deep learning methods have proved their capability to recognize irregularities in a wide range of research fields accurately. An excellent substitute for conventional methods of anomaly detection and classification is Convolutional Neural Networks (CNN). In this research, a novel IDS-based improved CNN model for IoT networks has been developed. To solve the issue of overfitting and improve the sophistication of the classifier, various regularization techniques, including L1, L2, Dropout, and multi-regularization, have been deployed. The experimental findings demonstrate that, when contrasted to the other CNN2D models, the proposed method outperforms with an above 98% accuracy. The Detection Rate and False Discovery Rate of individual classes are above 0.9 and below 0.1, respectively.