Real-Time Anomaly Detection of Advanced Persistent Threats Using Generative Adversarial Networks

Advanced Persistent Threats (APTs) pose a serious risk to modern digital systems because of their stealthy and multi-stage nature, which allows them to slip past traditional security defenses. This study introduces a real-time anomaly detection framework based on Generative Adversarial Networks (GANs) to accurately identify APT activities throughout their attack lifecycle. Unlike conventional detection models, the proposed system continuously learns normal network behavior and flags unusual patterns linked to reconnaissance, lateral movement, data theft, and initial compromise. A custom-built dataset capturing different APT behaviors, along with associated tactics, techniques, and indicators of compromise (IoCs), was used for model training and testing. The GAN design features a generator that replicates normal network traffic and a discriminator that identifies malicious deviations in real time. Through this adversarial learning process, the model effectively recognizes complex patterns and distinguishes between legitimate and suspicious activities. Experimental evaluations show that the model delivers high accuracy in detecting subtle and evolving APT attacks. By enabling real-time monitoring and full-stage threat coverage, this approach provides a reliable and scalable solution for proactive cybersecurity defense.