| dc.description.abstract | In the current digital era, there is a rising need for interconnected networks and communication systems, which raises the risk of cyberattacks, notably the Man-in-the-Middle (MitM) attack. An adversary intercepts and modifies communication between two parties during a MitM attack, potentially resulting in data breaches, privacy violations, and financial losses.This research studies different classifiers to overcome this problem by effectively utilizing artificial intelligence (AI) to detect man-in-the-middle attacks. The suggested method analyzes network traffic patterns and detects probable irregularities connected to MitM attacks using machine learning algorithms and deep neural networks. The AI model can learn sophisticated patterns and behaviors that enable it to discriminate between legal and compromised communications by being trained on a large dataset made up of benign and malicious traffic samples. | |