3.8 Proceedings Paper

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

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/1CPADS510-10.2020.00094

Keywords

traffic signal system; congestion attack; traffic flow; gated recurrent unit; security reinforcement

Funding

  1. National Natural Science Foundation of China [61972025, 61802389, 61672092, U1811264, 61966009]
  2. Fundamental Research Funds for the Central Universities of China [2018JBZ103, 2019RC008]
  3. Science and Technology on Information Assurance Laboratory [614200103011711]
  4. Guangxi Key Laboratory of Trusted Software [KX201902]

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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 ISIG, 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.

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