Published Paper Details:

The ever-evolving threat landscape in cyberspace necessitates advanced and adaptive security mechanisms. Traditional Intrusion Detection Systems (IDS) often struggle to detect novel and stealthy attacks due to their dependence on static rules or labeled data. This paper explores the integration of Generative Adversarial Networks (GANs) into autonomous IDS frameworks to enhance their ability to detect complex and unknown cyber threats. GANs, through their adversarial learning paradigm, enable the simulation of sophisticated attack patterns, which can be used to augment training datasets and improve the robustness of detection models. We propose a novel adversarial intelligence-driven IDS that dynamically evolves by learning from synthetic attack behaviors generated by the GAN. The experimental results demonstrate significant improvements in anomaly detection accuracy and false positive rate reduction, showcasing the potential of GANs to revolutionize intelligent threat detection. This approach provides a scalable and proactive defense mechanism, aligning with the future of autonomous cybersecurity systems.