Enhancing Cybersecurity in SCADA IoT Systems: A Novel Machine Learning-Based Approach for Man-in-the-Middle Attack Detection

Cybersecurity challenges are a real concern for any Internet of Things (IoT) systems, especially the industrial sector. Supervisory Control and Data Acquisition (SCADA) systems are one of the applications of the IoT that facilitate remote control of industrial systems, save time and effort, increase the accuracy of systems, and have many other advantages. With these facilities provided by these systems, cyber attackers are increasingly greedy about them for various reasons, including material, espionage, or sabotage, so strict security controls must be put in place to maintain the reliability of these systems. This paper proposes an ML-based model to detect Man In The Middle (MitM) attacks; we applied several artificial intelligence algorithms, this study showed high accuracy detection results of up to 98.22% using Random Forest, which indicates the possibility of applying it on the ground to enhance cyber security in SCADA industrial systems.