A Multi-Layered AI-Driven Cybersecurity Architecture: Integrating Entropy Analytics, Fuzzy Reasoning, Game Theory, and Multi-Agent Reinforcement Learning for Adaptive Threat Defense
Eram Fatima Siddiqui, Mohd Haleem, Sheikh Fahad Ahmad, Amina Salhi, Abu Taha Zamani, Naushad Varish · IEEE Access · 2025
In the face of increasingly sophisticated cyberattacks, including adaptive adversaries and stealthy anomalies, key features of defense mechanisms should be effective, interpretable, and theoretically rooted. Conventional intrusion detection systems are typically based on a single-paradigm machine learning model which can be effective (because it is optimized for conditions), but fail in generalizability and falling back on an explanation of its prediction. This paper outlines a multi-layered AI-enabled cyber defense framework that integrates entropy analytics, fuzzy inference, game-theoretic defense, and multi-agent reinforcement learning (MARL) inside a closed-loop adaptive architecture. In its simplest form, the novelty of the paper is that, four functional paradigms - uncertainty quantification, interpretability, strategic adversarial thinking, and live policy adaptation - are placed into a single coherent system. The framework operates as sequential and feedback salients - entropy analytics quantify the uncertainty in are states, fuzzy inference end maps the uncertainty into qualitative decision rules, game theory shapes defender - attacker towards equilibrium strategies, and MARL dynamically updates those strategies for convergence and long term adaptation. The empirical work on appropriate benchmark intrusion detection datasets consistently Thoutperformed baseline systems including the DDN, Fed-ID, AG-IDS, DL-FL systems producing a 6-12% increase in detection accuracy, lower false positive rates from non-intrusions, and a faster convergence, with adversarial examples across multiple epochs. Also, practical case studies reveal a level of improved explainability in threat classification and anomaly detection, which equates to practical interpretability for security analysts from the framework. The major contributions of the work are threefold: (i) an integrated multi-layered AI-based cybersecurity framework, (ii) theoretical robustness results in bounded adversarial models, and (iii) performance and interpretability form the systematic empirical evaluations over multiple datasets.