Abstract:

The application of Federated Learning (FL) with Blockchain Technology (BCT) and the resources of Artificial Intelligence (AI) enhances the management of secure computer networks with privacy and intelligent systems. Altogether, these technologies create a single framework for the governance and security of advanced distributed networks. Existing technologies have centralized points of control which are susceptible to cyber attacks, data silos, and trust issues among third parties. These cyber limitations in dynamic network systems impede their scalability, real-time response, and privacy. The paper proposes a model called Secure Federated Blockchain-AI Network Management (SeFBANM) to address these concerns. This framework integrates FL, BCT, and AI: FL allows learning to take place without providing raw data, BCT allows trustless and permanent log data provision among nodes, and AI adapts for threat detection and intelligent decision making. Policy enforcement is performed through the auto-generation of smart contracts that issue and amend policies. Consensus algorithms maintain data validity throughout the entire network. This methodology enables management without a central authority, reduces the dependence on dedicated control layers, and enhances the precision and responsiveness to security breaches in real-time. Furthermore, it allows privacy-preserving collaborative learning among edge devices using blockchain verification, ensuring confidentiality of user identity and data. Both the experimental evaluations and simulations indicate that SeFBANM outperforms traditional approaches in terms of data confidentiality, accuracy of intrusion detections, fault tolerance, and scalability. The framework employs an intelligent response strategy aimed at proficient secure network management with trust and confidence over increasingly complex scenarios.