Explainable Congestion Attack Prediction and Software-level Reinforcement in Intelligent Traffic Signal System

With connected vehicle(CV) technology, the next-generation transportation system is stepping into its implementation phase via the deployment of Intelligent Traffic Signal System (I-SIG). Since the congestion attack was firstly discovered in USDOT (U.S. Department of Transportation) sponsored I-SIG, deployed in three cities including New York, such realistic threat opens a new security issue. In this work, from machine learning perspective, we perform a systematic feature analysis on congestion attack and its variations from last vehicle of different traffic flow pattern. We first adopt the Tree-regularized Gated Recurrent Unit (TGRU) to make explainable congestion attack prediction, in which 32-dimension features are defined to character a 8-phase intersection traffic. We then develop corresponding software-level security reinforcements suggestions, which can be further expanded as an important work. In massive experiments based on real-world intersection settings, we eventually distill 384 samples of congestion attacks to train a TGRU-based attack prediction model, and achieve an average 80% precision. We further discussed possible reinforcement defense methods according to our prediction model.