Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods

The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabiliti...

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Autores principales:	Yingxiao Xiang, Wenjia Niu, Endong Tong, Yike Li, Bowei Jia, Yalun Wu, Jiqiang Liu, Liang Chang, Gang Li
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/58c69a09ca844173b044fa6cd652d09f
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spelling	oai:doaj.org-article:58c69a09ca844173b044fa6cd652d09f2021-11-08T02:37:01ZCongestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods1939-012210.1155/2021/1632825https://doaj.org/article/58c69a09ca844173b044fa6cd652d09f2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1632825https://doaj.org/toc/1939-0122The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabilities can be exploited to create congestion in an intersection and even trigger a cascade failure in the traffic network. To avoid this issue, timely and accurate congestion attack detection and identification are essential. This work proposes a congestion attack detection approach by combining empirical prediction and analytical verification. First, we collect a range of traffic images that correspond to specific traffic snapshots which are vulnerable to potential data spoofing attacks. Based on these traffic images, an improved generative adversarial network is trained to predict whether a forthcoming attack will cause congestion with a high probability. Meanwhile, we define a group of traffic flow features. After exploring features and conducting a thorough analysis, a TGRU (tree-regularized gated recurrent unit)-based approach is proposed to verify whether congestion occurs. When we find a possible attack that can cause congestion with high probability and subsequent traffic flows also prove congestion, we can say there is a congestion attack. Thus, we can realize timely and accurate congestion attack detection by integrating empirical prediction and analytical verification. Extensive experiments demonstrate that our approach performs well in congestion attack detection accuracy and timeliness.Yingxiao XiangWenjia NiuEndong TongYike LiBowei JiaYalun WuJiqiang LiuLiang ChangGang LiHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Yingxiao Xiang Wenjia Niu Endong Tong Yike Li Bowei Jia Yalun Wu Jiqiang Liu Liang Chang Gang Li Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods
description	The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabilities can be exploited to create congestion in an intersection and even trigger a cascade failure in the traffic network. To avoid this issue, timely and accurate congestion attack detection and identification are essential. This work proposes a congestion attack detection approach by combining empirical prediction and analytical verification. First, we collect a range of traffic images that correspond to specific traffic snapshots which are vulnerable to potential data spoofing attacks. Based on these traffic images, an improved generative adversarial network is trained to predict whether a forthcoming attack will cause congestion with a high probability. Meanwhile, we define a group of traffic flow features. After exploring features and conducting a thorough analysis, a TGRU (tree-regularized gated recurrent unit)-based approach is proposed to verify whether congestion occurs. When we find a possible attack that can cause congestion with high probability and subsequent traffic flows also prove congestion, we can say there is a congestion attack. Thus, we can realize timely and accurate congestion attack detection by integrating empirical prediction and analytical verification. Extensive experiments demonstrate that our approach performs well in congestion attack detection accuracy and timeliness.
format	article
author	Yingxiao Xiang Wenjia Niu Endong Tong Yike Li Bowei Jia Yalun Wu Jiqiang Liu Liang Chang Gang Li
author_facet	Yingxiao Xiang Wenjia Niu Endong Tong Yike Li Bowei Jia Yalun Wu Jiqiang Liu Liang Chang Gang Li
author_sort	Yingxiao Xiang
title	Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods
title_short	Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods
title_full	Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods
title_fullStr	Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods
title_full_unstemmed	Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods
title_sort	congestion attack detection in intelligent traffic signal system: combining empirical and analytical methods
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/58c69a09ca844173b044fa6cd652d09f
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Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods

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