Authors

Aziboledia Frederick Boye

Rivers State University, Port Harcourt, Nigeria

Onate Egerton Taylor

Rivers State University, Dept. of Computer Science, Port Harcourt-Nigeria

Vincent Ike Emeka

Rivers State University, Dept. of Computer Science, Port Harcourt-Nigeria

Emmanuel Okoni, Bennett

Rivers State University, Dept. of Computer Science, Port Harcourt-Nigeria

Abstract

With the growing integration of the Industrial Internet of Things, securing these systems from cyberattacks and cyber threats has become critical. This research proposes a novel real-time intrusion detection and prevention (IDPS) combining Convolutional Neural Networks (CNN) and Fuzzy Logic (FL) approach for the industrial IoT systems tested on the dataset. Implemented in Python programming language using the Jupyter environment, this hybrid model leverages CNN’s spatial feature extraction capabilities with Fuzzy Logic’s adaptability in handling data uncertainty, achieving an acceptable industrial accuracy of 92.5% for industrial IoT systems. The system also maintained an average low false positive rate (FPR) of 2.51%, underscoring its effectiveness in distinguishing benign from malicious activity within industrial IoT networks. Additionally, the model achieved an average detection rate (DR) of 92.9% during simulation, making the model viable and allowing a quick response to potential threats. The system’s latency achieved low metric average results measured in 1.207 µsec, or 0.001207 milliseconds given a less percentage of 7.14% latency average acceptable and good and excellent for most industrial IoT applications. These findings demonstrate that a CNN-Fuzzy Logic approach offers both high accuracy and efficiency, proving to be a promising for industrial cybersecurity in industrial IoT environments. 

Keywords

Intrusion Detection Industrial IoT Cybersecurity CNN Fuzzy Logic Machine Learning Dataset Real-Time Threat Prediction time

Citation of this Article

Aziboledia Frederick Boye, Onate Egerton Taylor, Vincent Ike Emeka, & Emmanuel Okoni, Bennett. (2025). A CNN-Fuzzy Logic Approach for Real-Time Intrusion Detection and Prevention in Industrial IoT Systems. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 2(10), 1-13. Article DOI: https://doi.org/10.47001/JAIET/2025.210001

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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