An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

Yike Li,Wenjia Niu,Bowei Jia,Jiqiang Liu,Endong Tong,Yingxiao Xiang,Zhen Han,Long Li,Zhili Zhou

doi:10.1155/2020/8823300

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.

Highlights

With the development of Internet-of-Things (IoT), transportation system is being transformed by various smart sensing devices and connected vehicle (CV) technology [1, 2]
Compared to traditional congestion prediction, the attack-based congestion prediction is totally different, and it is because any classical traffic flow-related theory such as traffic wave
Such approach enables a prediction from attack to corresponding consequence and provides an explanation from congestion to the initial traffic of attack phase (iii) We evaluate our approach empirically from real controlled optimization of phases algorithm (COP) algorithm through VISSIM and collect 4476 image samples of high quality for experiment, which shows the effectiveness of our approach compared to ground truth

Summary

Introduction

With the development of Internet-of-Things (IoT), transportation system is being transformed by various smart sensing devices and connected vehicle (CV) technology [1, 2]. An algorithm-level attack on controlled optimization of phases- (COP-) based [4, 5] intelligent signal system (I-SIG) [6] is exposed in 2018, in which through data spoofing of vehicle’s GPS location and speed, an attacker can compromise the vehicle-side units of a last vehicle existing with quite low attack cost, mislead the traffic control decisions at proper timing, causing unexpected heavy traffic congestion This worst result shows that one single attack vehicle is able to cause total congestion of 14 times higher [7]. It is highly important to analyze the traffic congestion attack caused only by one malicious vehicle instead of lots of vehicles, helping to provide effective defenses before wide deployment to the ground

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