Abstract
Computer networks are highly vulnerable to cybersecurity intrusions. Likewise, software-defined networks (SDN), which enable 5G users to broadcast sensitive data, have become a primary target for vulnerability. To protect the network security against attacks, various security protocols, including authorization, the authentication process, and intrusion detection techniques, are essential. However, there are several intrusion detection strategies, but the most prevalent methods show low accuracy and high false positives. To overcome these problems, this research work presents a novel four-Q curve authentication system based on Media Access Control (MAC) addresses for a multilayered SDN intrusion detection system utilizing deep learning techniques to identify and prevent attacks. The Four-Q curve authentication system leverages elliptic curve cryptography, a high-performance algorithm that improves authentication security and computational efficiency. Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. Further, the optimization of DDcGAN is accomplished using the Sheep Flock Optimization Algorithm (SFOA), whereas suspicious packets are categorized using the Growing Self-Organizing Map (GSOM). The DDcGAN-based intrusion detection system outperforms the state-of-the-art approaches in terms of accuracy, precision, F1 score, sensitivity, false-positive rate, power consumption, and network throughput. It achieved an accuracy of 98.29%, an F1 score of 0.975, and a precision of 95.8%. The system's true positive rate attained 99.04% at 50% malicious nodes, while the false alarm rate was as low as 2.05% under the same conditions. Moreover, the system exhibits 4.5% energy savings when compared to existing approaches.