Dynamic cyber deception using AI-driven adaptive honeypot networks and context-aware behavioural analysis for proactive threat neutralization

The cybersecurity world is tackling a number of sources of advanced threats such as zero-day exploits, polymorphic malware, and APTs that make the classical static defenses inefficient so these days the situation is really tough for the cybersecurity experts. The introduction of a framework called Adaptive Swarm-Driven Cyber Deception with XAI-Aided TransGraph Learning (ASD-TGXAI) as a new way of dealing with these challenges is one of the solutions of the present study. The framework combines dynamic honeypots, adaptive deception strategies, and AI-powered real-time analysis to develop a progressive defense system. Unlike static approaches, the ASD-TGXAI framework dynamically adapts to evolving threats by employing the Adaptive Swarm-Reinforce Evolutionary Framework (ASREF), which leverages Swarm Intelligence, Reinforcement Learning, and Differential Evolution for optimization. In addition, the TransGraph-XAI Fusion Model (TGXAI-Net) combines Transformers, Graph Neural Networks (GNNs), and Explainable AI (XAI) to enhance the accuracy and interpretability of threat predictions. The dynamically changing ASD-TGXAI framework contrasts well with the traditional static defensive mechanisms, where changes in honeypot behavior are effected on the basis of real-time threat intelligence and interaction patterns of the attackers, thus, enabling threat mitigation to be done in a proactive way rather than reactive. The use of the XAI modules in every decision guarantees that the decisions are made in a transparent manner, the decisions can be traced and are also interpretable by the cybersecurity analysts, hence, this being the first-of-its-kind a creative contribution to the intelligent deception-based defense systems. Effectively, the false positives and false negatives are minimized to 0.02, ensuring a 98% accuracy in the detection of threats along with a significant decrease in threat neutralization time to 200 ms. It has an adaptability score of 10%, a resource utilization efficiency of 90%, and deception effectiveness of 95%, thus ensuring reliable performance and resilience in increasingly dynamic environments. This study not only improves cybersecurity defenses but also sets up a feedback-driven environment for sustained intelligence and proactive approaches.