Dynamic Neuro-Fuzzy Vulnerability Detection System (DNF-VDS)

The rapid expansion of Industrial Internet of Things (IIoT) infrastructures has introduced a complex, heterogeneous ecosystem that is increasingly vulnerable to diverse cyberattacks. Traditional machine learning- and deep learning-based intrusion detection systems provide strong predictive performance but lack interpretability, rely on static models, and fail to adapt to evolving attack patterns. This study proposes a Dynamic Neuro-Fuzzy Vulnerability Detection System (DNF-VDS) that integrates fuzzy rule-based reasoning with neural network-driven parameter optimization. The system supports automatic rule generation, continuous rule updating, and membership function adaptation, enabling interpretable and adaptive vulnerability detection suited for dynamic IIoT environments. Using the Edge-IIoTset dataset, the proposed system achieves 95% accuracy, 93.5% F1-score, and a false positive rate of 5%, outperforming baseline models, including Bidirectional Long Short-Term Memory, Support Vector Machine, Random Forest, and Decision Tree. Statistical significance testing confirms that improvements are non-random (p < 0.05). Unlike prior neuro-fuzzy approaches, DNF-VDS demonstrates quantifiable interpretability through rule analysis and activation profiling while maintaining scalability for industrial deployment. These findings establish DNF-VDS as a transparent, adaptive, and high-performing solution for IIoT vulnerability detection. Future work will explore real-time deployment and distributed learning across industrial nodes.