AI in Combatting Man-in-the-Middle Attacks: A Comprehensive Review
Nachaat Mohamed, Abdussalam Ali Ahmed
Abstract
In the evolving landscape of cybersecurity, Man-in-the-Middle (MitM) attacks represent a significant and persistent threat, capable of intercepting and manipulating digital communications in a clandestine manner. This comprehensive review paper delves into the burgeoning role of Artificial Intelligence (AI) in identifying, mitigating, and preventing these sophisticated cyber threats. We explore the multifaceted nature of MitM attacks, including their methodologies, targets, and potential impacts, setting the stage for a deeper understanding of the necessity for advanced countermeasures. The core of this review illuminates the innovative AI-based strategies currently being employed and developed. These strategies encompass machine learning algorithms, pattern recognition, and predictive modeling, tailored specifically to detect anomalies and patterns indicative of MitM intrusions. We further investigate the integration of AI with existing cybersecurity frameworks, highlighting enhancements in real-time threat detection, response efficiency, and adaptive learning capabilities. The paper concludes by discussing the challenges and future prospects in this domain, emphasizing the dynamic interplay between evolving AI technologies and the perpetually shifting landscape of cyber threats. In this review, our goal is to offer a thorough understanding of the present situation and possible future trends of AI applications in addressing Man-in-the-Middle attacks. This serves as an important tool for researchers, practitioners, and policymakers involved in cybersecurity.