Chaos Mapping and Marine Predators Algorithm-Based Deep Learning Framework for Intrusion Detection in IIoT Networks

The Industrial Internet of Things (IIoT) extends IoT applications to industrial environments, driving significant enhancements in operational efficiency. However, this evolution brings heightened cybersecurity risks, posing challenges to the protection of IIoT systems. To address these issues, this study introduces a novel Chaos Mapping and Marine Predators Algorithm-based Deep Learning Intrusion Detection System (CMPADL-IDS). The proposed model employs a two-stage process: feature selection using Chaos Mapping and the Marine Predators Algorithm (CMPA) and anomaly detection using a Long Short-Term Memory Autoencoder (LSTM-AE). The CMPA effectively identifies optimal features by leveraging chaotic systems and MPA's intelligent optimization capabilities. For enhanced performance, Bayesian Optimization (BO) is employed to fine-tune LSTM-AE hyperparameters, optimizing detection accuracy and computational efficiency. The framework was tested on the ToN-IoT dataset and managed to reach an average accuracy of 98.40%, a precision of 80.30%, a recall of 77.80%, an F1-score of 78.62% and an AUC score of 88.80%. The evaluation proves that using the suggested feature selection and anomaly detection techniques improves IIoT network security more than existing methods.