Detection of Man-in-the-Middle Attack Through Artificial Intelligence Algorithm

The amalgamation of information technologies and progressive wireless communication systems has profoundly impacted various facets of everyday life, encompassing communication mediums, occupational procedures, and living standards. This evolution, combined with enhanced wireless communication quality, has culminated in an exponential rise in interconnected devices, including domestic appliances, thereby birthing the Internet of Things (IoT) era. This proliferation, facilitated by cloud computing enabling remote device control, concurrently intensifies cybersecurity threats. Traditional Information and Communication Technology (ICT) architectures, characterized by a hub-and-spoke model, are inherently vulnerable to illicit access and Man-in-the-Middle (MITM) intrusions, thereby endangering information confidentiality. Leveraging Artificial Intelligence (AI) can ameliorate this scenario, enhancing threat training and detection capabilities, enabling precise and preemptive attack countermeasures. This research underscores the criticality of addressing the security implications accompanying technological advancements and implementing protective measures. Deploying AI algorithms facilitates efficient passive attack identification and alleviates network device burdens. Specifically, this study scrutinized the ramifications of an MITM attack on the system, emphasizing the detection of this elusive threat using AI. Our findings attest to AI’s efficacy in detecting MITM attacks, promising significant contributions to future cybersecurity research.