An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method. (15th December 2020)

• Record Type: Journal Article
• Authors: Li, Yike; Xiang, Yingxiao; Tong, Endong; Niu, Wenjia; Jia, Bowei; Li, Long; Liu, Jiqiang; Han, Zhen
• Other Names: Zhou Zhili Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2020(2020)
• Publication Date: 2020-12-15
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2020/8823300
• Languages: English
• ISSNs: 1530-8669

Abstract

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.