Autonomous Blockchain-Enabled Security Framework for Smart Grids Using Adaptive AI

Abstract

The increasing interconnectivity of smart grids exposes critical energy infrastructure to more sophisticated cyber threats, necessitating adaptable and auditable security measures. This study presents a blockchain-enabled, self-improving intrusion detection system (IDS) that integrates a permissioned blockchain, autonomous governance loops, and a hybrid CNN–LSTM detector. The platform retrains models across federated nodes using blockchain-anchored data, facilitates automatic containment through smart contracts, and permanently stores validated alarms. Following multiple self-improvement cycles, the system enhances its performance from an initial 94.5% accuracy and 4.2% false positive rate (FPR) to 98.1% accuracy, a 97.6% detection rate (recall), and a 2.1% FPR in simulated tests. In comparison to baselines, a blockchain-only IDS recorded 94.1% accuracy with a 4.8% FPR, while a conventional machine learning-based IDS achieved 92.7% accuracy with a 5.4% FPR. Operationally, blockchain anchoring provided a throughput of approximately 1,200 transactions per second with an average transaction latency of about 1.5 seconds. The combined detect-to-contain latency for high-severity events was approximately 3.2 seconds. These findings demonstrate that a scalable, low-FPR, and rapid-response security paradigm for modern smart grids can be achieved by integrating adaptive artificial intelligence with decentralized, robust governance.