Self-adaptive cyber deception and resilient network defense via adversarial environment simulation

Abstract In the dynamic world of cybersecurity, the attacker is constantly innovating his methods of attack while taking advantage of vulnerable unknown to defender reactive defense is increasingly inadequate. This paper presents a novel approach for proactive cyber defense- autonomous system, which combines cyber deception, adversarial environment simulation, and self-adaptive reinforcement intelligence. At the core of this approach are dynamic and life-like attack vectors created through Generative Adversarial Networks (GANs) to mimic real-world zero-day and polymorphic threats. These artificially adversarial scenarios make the training environment very unstable for the DRL agent in the sense it has to learn robust context aware defense policies when the enemy is changing its attack strategies on the fly. Through a sequence of sustained exchange with GAN-generated environment, the DRL agent can be trained to identify malice with a non-trivial set of malware behaviors beyond finite rules or static signatures. Whereas traditional IDS/IPS solutions (i.e., performed respectively after-the-fact and by rule-based actions) simply aim to mitigate an attack, the latter moves the goalposts by transforming the surface of attack-defense continuously into a battlefield, and by employing, among others, deception nodes, re-routing detection vectors, and risk posture adaptation as the threat context changes. The observer is not only robust to novel attacks but also triggers deceptive traps on the attacker and confounds attackers’ inference paths, leading to increased robustness as well as lower false alarms and reduced compromise-recovery time. We run our method on a synthetic smart-grid network where we inject good and adversarial traffic into the network so that we can evaluate the resilience under multiple attack scenarios in a fine-grained manner. Empirical study in various network scenarios shows that the effectiveness of proactive threat detection, defense strategy optimization and recovery performance can be significantly improved MTTC by approximately 4.5× compared to static IDS, while reducing FPR by over 75%, demonstrating substantial gains in both detection reliability and response efficiency. The proposed model serves as a cornerstone toward the development an autonomous, digital immune system that learns, evolves, and turns even the most lethal forms of malware, ransomware, and zero-day attacks into benign files without human intervention—to move from static defense to dynamic, intelligent cyber-resilience.