IoT-MGSec: Mitigating Man-in-the-Middle Attacks in IoT Networks Using Graph-Based Learning

The Internet of Things presents a transformative era of device connectivity while creating a new paradigm shift in the process. This, however, has been met with some major pitfalls, such as an increase in device insecurity, characterized by Main-in- The Middle (MiTM) attacks. In this paper, we propose IoT-MGSec, a novel solution to mitigating MiTM attacks using graph-based learning. Our approach employs graph modeling and embedding techniques to learn node and edge features such that we can generate a robust classifier to detect MiTM attacks with high accuracy. We validate the effectiveness of our approach by comparing its performance to baseline models, and the results indicate that our approach outperforms the baseline models. The findings suggest that this approach offers a more robust solution to detecting and mitigating Man-in-the-middle attacks, and it holds potential for integration into real-time intrusion detection systems, further enhancing its capacity to secure devices within the loT landscape.