Deep Learning-Based Intrusion Detection Systems

Abstract

Given the increasing growth of cyber-attacks, the need for intrusion detection systems (IDS) with higher accuracy and efficiency is critical. This paper presents a novel approach using Generative Adversarial Networks (GANs) for intrusion detection. The proposed model leverages deep learning to extract complex features and uses GANs to generate synthetic data, improving IDS accuracy and efficiency. This approach reduces false positive and negative rates while increasing the accuracy of detecting unknown attacks. Experimental results on the NSL-KDD and CICIDS2017 datasets show 98.2% accuracy, a 1.5% false positive rate, and a 0.8% false negative rate, outperforming conventional methods. These results confirm that GANs can significantly improve the detection and classification of cyber-attacks. The proposed method is an effective solution to enhance cybersecurity and reduce cyber-attack risks, demonstrating significant improvements in IDS and paving the way for future research in this area.