The Convergence of Blockchain and Artificial Intelligence in Securing Industrial Control Systems and Future Network Architectures: A Comprehensive Theoretical and Empirical Analysis

The rapid evolution of Industrial Control Systems (ICS) and the emergence of 6G communication paradigms have introduced unprecedented complexities in data integrity, resource sharing, and anomaly detection. Traditional security frameworks often struggle with the centralized nature of trust and the static logic of conventional intrusion detection. This research explores the synergistic integration of blockchain technology and Artificial Intelligence (AI) to establish a decentralized, immutable, and autonomous security architecture. By leveraging blockchain’s inherent transparency and AI’s predictive capabilities-specifically focusing on Graph Neural Networks (GNNs), Transformers, and Autoencoders-this study proposes a holistic framework for securing Industry 4.0 environments. We examine the role of game-theoretic consensus protocols in achieving true decentralization and the deployment of deep learning models for real-time anomaly detection in complex datasets like SWaT and WADI. The findings suggest that while blockchain ensures the integrity of the audit trail and facilitates secure resource sharing in 6G, AI provides the necessary intelligence to identify sophisticated cyber-physical attacks. This paper concludes that the convergence of these technologies is not merely an incremental improvement but a fundamental shift toward self-healing, resilient digital infrastructures.