Volume 15 , Issue 2 , PP: 208-224, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mounir Mohammad Abou-Elasaad 1 * , Samir G. Sayed 2 , Mohamed M. El-Dakroury 3
Doi: https://doi.org/10.54216/JCIM.150216
The world is witnessing an unprecedented boom in the development of information technology, which has come to encompass all aspects of life, Smart networks based on the Industrial Internet of Things (IIoT) are among the latest technologies used in various industries, contributing to improved production efficiency, reduced costs, and enhanced security, With the increasing reliance on this technology, the challenge of complex cyberattacks are also on the rise, These attacks are considered one of the major challenges facing smart networks, as attackers can exploit vulnerabilities in systems to access sensitive data or disrupt industrial operations, To counteract these threats, advanced intrusion detection systems should be developed, leveraging artificial intelligence and big data analytics to effectively detect and respond to attacks in real-time. Therefore, it is imperative to strive towards developing advanced and intelligent security systems to combat cyberattacks, ensuring the safety of industrial operations and data protection. This paper provides two IDS based on AI that are developed to negate the raising sophisticated cyberattacks. IN the first technique, Group of ML techniques such as Decision tree, Random Forrest classifiers, support vector classifier, and K_Nearest Neigbor are used with Feature reduction algorithms classifying network traffic subspecies to enhancing the accuracy and efficiency of detection systems. The second proposed technique for specifying the type of intrusion advantage various methodologies, particularly in the context of IoT networks and deep learning, the two algorithms are trained and tested using three well-known datasets to investigate wide domain of cyberattacks targeting the IIoT infrastructure. Results of the simulation show that the algorithm proposed in this work provides high improvement in detection of cyberattacks. The first algorithm achieved an accuracy of 99.9% and a very low false positive rate of just 0.1%. In addition, the second proposed algorithm identifies type of attack with a detection ratio of 99.76%. These results demonstrate how the proposed IDS based on AI algorithms can effectively detect network intrusion, and significantly enhance the security of IIoT system
Industrial Internet of Things (IIoT) , Intrusion Detection Systems (IDS) , Artificial Intelligence (AI) , Machine Learning (ML), Deep Learning (DL)
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