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- Title
An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method.
- Authors
Li, Yike; Xiang, Yingxiao; Tong, Endong; Niu, Wenjia; Jia, Bowei; Li, Long; Liu, Jiqiang; Han, Zhen
- Abstract
With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach.
- Subjects
TRAFFIC signs &; signals; TRAFFIC incident management; TRAFFIC engineering; TRAFFIC flow; TRAFFIC congestion; TRAFFIC safety; PROCESS optimization; FORECASTING
- Publication
Wireless Communications & Mobile Computing, 2020, p1
- ISSN
1530-8669
- Publication type
Article
- DOI
10.1155/2020/8823300