AIoT-Blockchain Security for Supply Chain Threats in IEC 61850 Substations Using Informer-Powered Reinforcement Learning

Lilia Tightiz, Lien Minh Dang, Ki-Woong Park

Abstract: IEC 61850 substations enable fast-speed digital communication among intelligent electronic devices (IEDs) for power system automatic control, monitoring, and protection. Their remote configurability and interoperability, however, make them vulnerable to highly advanced cyberattacks, mainly supply chain attacks. While existing methods, such as intrusion detection systems (IDSs) and machine learning (ML)-based anomaly detection, provide partial protection, they often lack resilience against evolving attacks and real-time mitigation capabilities. We present an Artificial Intelligence of Things (AIoT)–blockchain security framework that uses Informer-augmented proximal policy optimization (PPO) for adaptive cyber defense, along with Hyperledger Fabric, for tamper-proof and automated security enforcement. The novelty of the proposed framework over state-of-the-art research lies in its combination of anomaly detection, dynamic threat mitigation, and auditable policy execution. Our security tests demonstrate robustness against zero-day and synthetic adversarial attacks while preserving privacy and integrity. Experimental findings demonstrate that Informer-PPO attains 98.4% detection accuracy and 35-ms response time, representing improvements of 3.6%, 5.0%, and 9.1% in accuracy and 32.7%, 51.4%, and 63.2% faster response time compared to Transformer-PPO, long short-term memory (LSTM)-PPO, and convolutional neural network (CNN)-PPO baselines, respectively. Blockchain-enabled policy enforcement is accomplished within 42–50 ms, facilitating scalable real-time protection for IEC 61850 substations.