Performance Analysis of a CNN-Fuzzy Logic Based Real-time Intrusion Detection for Industrial IoT Systems

The Industrial Internet of Things has enhanced automation, real-time monitoring, and predictive decision-making in modern industries. The study explores the mixed research methods (qualitative and quantitative). However, the growing connectivity of industrial IoT systems has exposed them to severe cyber threats such as Ransomware, MitM, and DDoS attacks, which can disrupt critical operations and compromise safety. Conventional Intrusion Detection Systems (IDS) often face limitations in achieving high accuracy, rapid detection, and low latency while minimizing false alarms. This study proposes a CNN-Fuzzy Logic hybrid model for real-time intrusion detection and prevention in industrial IoT environments. Convolutional Neural Networks (CNN) are employed to extract deep hierarchical features from industrial IoT traffic, while fuzzy logic is integrated to enhance decision-making under uncertainty and reduce false positives. The model was trained and evaluated using Kaggle cybersecurity datasets containing ransomware, MitM, and DDoS attacks. Performance evaluation demonstrates that the CNN-Fuzzy IDS achieves an accuracy of 92.5%, a detection rate of approximately 93%, a false positive rate (FPR) of 2.51%, a reduced latency with an average of 7.14% total latency (which corresponds to 1.207 µsec average latency) is very acceptable for most industrial IoT applications. These results highlight the effectiveness of hybrid intelligent systems in enhancing the resilience and reliability of industrial IoT cybersecurity. The proposed model provides a promising pathway for deploying scalable, adaptive, and real-time IDS solutions in critical industrial infrastructures. On system computational overhead researchers should employ a minimum practical setup with modern multi-core CPU, 8–16 GB RAM, SSD, stable OS (Windows 10 only if hardware is modern) or run a lightweight Linux on edge plus offload heavy tasks elsewhere. Future research should also focus on optimizing hybrid ML architectures for low performance metrics for deployment of resource-constrained industrial IoT devices, integrating the approach for threat detection, and expanding evaluation to real-world industrial environments.