Cognitive AI for Network Resilience: Integrating Explainable AI and Blockchain for Real-Time Cyber Threat Detection

Abstract: The increasing complexity of cyber threats requires intelligent, adaptive, and transparent security solutions capable of real-time detection and response. This review paper examines the integration of Cognitive Artificial Intelligence (AI), Explainable AI (XAI), and blockchain technology to enhance network resilience against evolving cyberattacks. Recent research on AI-driven threat detection, XAI techniques such as SHAP and LIME, cyber resilience frameworks, and blockchain-based secure logging is analyzed to identify current advancements and research gaps. A conceptual Cognitive AI for Network Resilience (CAINR) framework is proposed, combining deep learning-based threat detection, explainable decision-making, and blockchain-enabled immutable audit trails. The study highlights the strengths and limitations of existing approaches and demonstrates that no single solution currently provides high detection accuracy, interpretability, secure logging, and automated response simultaneously. Future directions, including federated learning and reinforcement learning-based self-healing networks, are discussed. This review provides a foundation for developing trustworthy and resilient next-generation cybersecurity systems.

Keywords: Cognitive AI, Network Resilience, Explainable AI, XAI, Blockchain, Cyber Threat Detection, SHAP, LIME, Intrusion Detection Systems, Machine Learning, Cyber Resilience Framework, Zero-Day Attacks, Industrial IoT, SCADA Security, Threat Intelligence.