An adaptive hybrid framework for IIoT intrusion detection using neural networks and feature optimization using genetic algorithms

In Industrial Internet of Things (IIoT) networks, securing device connectivity through effective intrusion detection systems is essential for maintaining operational integrity. This paper presents an adaptive hybrid framework for IIoT intrusion detection that combines Artificial Neural Networks (ANNs) with Genetic Algorithms (GA) for feature optimization. This study utilizes the dataset, which is widely recognized benchmark for intrusion detection research. The dataset comprises 625783 network traffic samples, classified into five categories: Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), User-to-Root (U2R), and Normal traffic. Initially, an ANN model was developed, yielding high accuracy but exhibiting signs of overfitting. To enhance generalization, we introduced L2 regularization, adjusted dropout rates, and optimized the learning rate, ultimately achieving a 99.7% validation accuracy with an AUC score of 0.9969. Additionally, Genetic Algorithms were employed to optimize feature selection, further refining the ANN’s input space to improve computational efficiency without sacrificing predictive power. After training over 50 epochs with early stopping, the model demonstrated exceptional robustness, achieving 99.5% accuracy on the test set, with precision and recall values of 0.97 and 0.98, respectively. This combination of ANN and GA yielded a highly efficient and sensitive framework, providing enhanced detection of anomalies in IIoT environments. The proposed hybrid model thus establishes a robust solution for real-time IIoT security, outperforming conventional detection systems through a strategic blend of neural learning and evolutionary optimization.