An Intelligent Deep Learning Framework With Progressive Graph Convolutional Networks for Intrusion Detection in Industrial IoT Networks

ABSTRACT

The industrial Internet of things (IIoT) integrates IoT technologies into industrial environments, enhancing automation, monitoring, and manufacturing efficiency. However, interconnected devices and sensors introduce significant security and privacy challenges, making intrusion detection a critical requirement. This study proposes a comprehensive progressive graph convolutional networks approach for enhancing the security of IIoT through advanced intrusion detection systems for threat identification and mitigation (PGCN‐IoT‐AIDS‐TIM). Data were collected from the ToN_IoT dataset and preprocessed using a regularized bias‐aware ensemble Kalman filter (RBAEKF) to clean, normalize, and balance the data. Relevant features were selected using the multiplayer battle game‐inspired optimizer (MBGIO). The selected features were then classified using progressive graph convolutional networks (PGCN) to identify normal and attack traffic. To improve detection accuracy, the Bitterling Fish Optimization Algorithm (BFOA) was employed to optimize PGCN parameters. The proposed framework was implemented in Python and evaluated using accuracy, precision, recall, F ‐measure, specificity, ROC, error rate, and computational time. Results demonstrated superior performance compared with CNN‐ID‐IoT, DNN‐AI‐ID‐IoT, and LSTM‐IDA‐IoT methods, achieving substantial improvements in accuracy, precision, and recall.