Queue Length Estimation Based Defence Against Data Poisoning Attack for Traffic Signal Control

Gao, Xu; Liu, Jiqiang; Li, Yike; Wang, Xiaojin; Xiang, Yingxiao; Tong, Endong; Niu, Wenjia; Han, Zhen

doi:10.1007/978-3-030-46931-3_24

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Gao et al [150] proposed queue length estimation-based defense against data poisoning attacks that can be a serious threat for intelligent transportation system systems. The authors analyze the characteristics of the single-point attack and increase the number of attack points to analyze the system's vulnerabilities.…”

Section: Defenses Against Ai/m Attackmentioning

confidence: 99%

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Gupta¹,

Maple²

2023

Preprint

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<p>Edge Computing-based Connected Autonomous Vehicles (EC-CAVs) can fulfill the emerging demand for smart transportation on a global scale. Such innovations to transportation ecosystems can bring manyfold benefits from fully autonomous driving services to proactive vehicle monitoring and traffic management. Given the complexity involved in the deployment of EC-CAVs, zero-tolerance safety and security measures must be incorporated to avert vehicle immobilization, road accidents, disclosure of sensitive data, or any potential threats. This paper presents a reference architecture of EC-CAVs to investigate existing and emerging cyber-threats for deriving a common attack taxonomy for the EC-CAVs ecosystem. Subsequently, a comprehensive survey of security mechanisms is presented that can ensure safe and secure transportation of passengers from one destination to another using an EC-CAVs ecosystem.</p>

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Section: Defenses Against Ai/m Attackmentioning

confidence: 99%

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Gupta¹,

Maple²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Gao et al [83] propose queue length estimation-based defense against data poisoning attacks that can be a serious threat to intelligent transportation systems. The authors analyze the characteristics of the single-point attack and increase the number of attack points to analyze the system vulnerabilities.…”

Section: ) Defenses Against Replay Attackmentioning

confidence: 99%

An Investigation of Cyber-Attacks and Security Mechanisms for Connected and Autonomous Vehicles

Gupta,

Maple,

Passerone

2023

IEEE Access

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Connected and autonomous vehicles (CAVs) can fulfill the emerging demand for smart transportation on a global scale. Such innovations for transportation can bring manyfold benefits, from fully autonomous driving services to proactive vehicle monitoring and traffic management. However, given the complexity involved in the deployment of CAVs, zero-tolerance safety, and security measures must be incorporated to avert vehicle immobilization, road accidents, disclosure of sensitive data, or any potential threats. In this article, we conceive a reference architecture for a CAVs ecosystem to derive a common attack taxonomy for the investigation of existing and emerging cyber threats. Subsequently, we discuss security mechanisms for the CAVs ecosystem that can be useful for the safe and secure transportation of passengers from one destination to another based on comprehensive studies of academic literature and industry white papers. Our work can provide valuable insights to security engineers and system architects for investigating security problems using a top-to-bottom approach and can aid in envisioning robust security solutions to ensure seamless CAVs operations.

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“…. , G r,0 max do (16) tG r,0 � i (17) tG r,1 � effectG r − tG r,0 (18) if tG r,1 ≥ G r,1 min and tG r,1 ≤ G r,1 max then (19) Delay r � f(r, planP r,0 , planP r,1 , tG r,0 , tG r,1 , x j , A t,k ) //Calculated by Algorithm 2 (20) if Delay r < optV r then (21) optV r � Delay r (22) optG r,0 � tG r,0 (23) optG r,1 � tG r,1 (24) end if (25) end if (26) end for (27) : end for (28) else (29) v j � 99999 (30)…”

Section: High-level Image Feature-based Congestion Attackmentioning

confidence: 99%

“…We do not suggest providing two alternative versions of EVLS (i.e., one for high and one for low PR, respectively). Although we analyzed a car-following model in work [21], we believe that a more useful model with a collaboration mechanism should be studied; this will make the estimation of EVLS more accurate as well as COP security more robust.…”

Section: Defense Suggestionsmentioning

confidence: 99%

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

Xiang

Niu

Tong

et al. 2021

Security and Communication Networks

Self Cite

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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.

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Queue Length Estimation Based Defence Against Data Poisoning Attack for Traffic Signal Control

Cited by 5 publications

References 15 publications

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

An Investigation of Cyber-Attacks and Security Mechanisms for Connected and Autonomous Vehicles

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

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