Name
Title | ||
|---|---|---|
Explainable Congestion Attack Prediction and Software-level Reinforcement in Intelligent Traffic Signal System |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICPADS51040.2020.00094 | 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) |
Keywords | DocType | ISSN |
traffic signal system,congestion attack,traffic flow,gated recurrent unit,security reinforcement | Conference | 1521-9097 |
ISBN | Citations | PageRank |
978-1-7281-8382-4 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojin Wang | 1 | 0 | 0.34 |
| Yingxiao Xiang | 2 | 0 | 1.35 |
| Wenjia Niu | 3 | 178 | 30.33 |
| Endong Tong | 4 | 5 | 6.96 |
| Jiqiang Liu | 5 | 315 | 52.31 |