Demo: A Calibrated, Open-Source Toolkit for MitM Cyberattacks Visualization, Analysis, and Traffic Optimization at Connected Intersections

Man-in-the-middle (MitM) attacks pose significant threats to intelligent transportation systems by altering or fabricating traffic information that drivers and automated systems rely on. While most research has concentrated on securing the underlying communication protocols, comparatively little attention has been paid to the realistic operational consequences of these attacks and the transportation engineering strategies that could mitigate them. To fill this gap, we present an open-source toolkit, ICVAT, tightly integrated with SUMO for system-wide analysis. Our platform employs a calibrated driver model-derived from real-world data to provide credible safety and efficiency evaluations. Modular components address attack design, reinforcement learning-based detection, traffic flow-based optimization, and visualization, enabling users to conduct detailed what-if analyses at both the individual and network levels. By capturing complex driver behaviors and intersection dynamics, the toolkit supports research, education, and industry demonstrations, illuminating how cyberattacks ripple through traffic streams and identifying effective countermeasures.