AI-POWERED BEHAVIORAL ACCESS CONTROL FRAMEWORK USING SMART CONTRACTS ACROSS SDN ENVIRONMENTS

Abstract

This research investigates the integration of artificial intelligence with blockchain-based smart contracts to create dynamic access control systems that adapt to evolving user behavior patterns. We propose a novel framework that leverages generative AI models to analyze user interactions across multi-domain Software-Defined Networking (SDN) environments and automatically adjust access permissions through blockchain smart contracts. Our approach addresses two critical research questions: (1) how can blockchain-based identity management scale effectively across multi-domain SDN environments? And (2) How accurate are generative AI models in modeling and predicting malicious insider behavior? Through empirical evaluation across three enterprise networks with 5,724 users, we demonstrate that our proposed system achieves 94.3% accuracy in anomaly detection while reducing administrative overhead by 76% compared to traditional role-based access control systems. The framework shows significant improvements in scalability with a throughput of 1,450 transactions per second while maintaining security posture across federated domains.

Keywords: Smart Contracts, Access Control, Behavioral Analysis, Artificial Intelligence, Blockchain, Software-Defined Networking, Zero-Trust Architecture, Insider Threat Detection