Journal
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
Volume 2020, Issue -, Pages -Publisher
WILEY-HINDAWI
DOI: 10.1155/2020/8823300
Keywords
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Funding
- National Natural Science Foundation of China [61972025, 61802389, 61672092, U1811264, 61966009]
- Fundamental Research Funds for the Central Universities of China [2018JBZ103, 2019RC008]
- Science and Technology on Information Assurance Laboratory [614200103011711]
- Guangxi Key Laboratory of Trusted Software [KX201902]
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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.
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