Explainable Deep Learning Framework for IIoT Intrusion Detection Using CIC-IIoT 2025

The research tries to build a smart system that can detect cyber-attacks in industrial IoT networks. Conventional intrusion detection mechanisms do not cope with traffic of high dimensions and imbalance IIoT. In response to this problem, a deep learning-based framework combined with the hybrid nature-inspired optimization is established with the help of CIC-IIoT 2025 dataset. The optimization approach improves the choice of features and hyperparameter optimization, which boosts the detection and model generalization. Explainable Artificial Intelligence (XAI) methods are implemented to guarantee additional clear and understandable decision-making. The results of the experiment show better detection accuracy, equal precision and recall, and lower false alarm rates. The suggested system offers an optimized, scalable, and explainable cybersecurity system to the contemporary IIoT architectures.