Enhancing Security through a Machine Learning Approach to Mitigate Man-in-the-Middle Attacks

This abstract presents a thorough examination of enhancing security protocols by utilizing machine learning methods to prevent the risks associated with Man-in-the-Middle (MitM) attacks. This work focuses on assessing the effectiveness of three well-known classifiers, namely Random Forest, Support Vector Machine (SVM), and Naive Bayes, in the detection and prevention of MitM attacks.A Man-in-the-Middle (MitM) assault is a type of cyber-attack when an unauthorized entity intercepts and potentially modifies the communication between two parties without their awareness or permission. This poses a substantial security risk in several digital interactions. The comparison research highlights Random Forest as the most effective performer overall, showcasing its excellence in increasing security through its ability to accurately detect and prevent potential assaults. The results of this study provide significant contributions to understanding and implementing machine learning algorithms, specifically Random Forest, as an effective mitigation strategy against Man-in-the-Middle (MitM) vulnerabilities.