Vulnerability of fixed-time control of signalized intersections to cyber-tampering

Ghafouri, Amin; Abbas, Waseem; Vorobeychik, Yevgeniy; Koutsoukos, Xenofon

doi:10.1109/rweek.2016.7573320

Cited by 17 publications

(14 citation statements)

References 11 publications

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“…Laszka et al performed a theoretical analysis to estimate the potential congestion an attacker can create assuming that she can arbitrarily compromise multiple signal controllers [30]. A follow-up study was then performed for the same attack goal but with a weak assumption, in which the attacker can only compromise the sensors that collects traffic flow information [26]. In comparison, neither of these work analyzes the CV-based signal control scenario targeted in our work.…”

Section: Related Workmentioning

confidence: 99%

Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control

Chen

Yu-cheng²,

Feng

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

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Abstract-Connected vehicle (CV) technology will soon transform today's transportation systems by connecting vehicles and the transportation infrastructure through wireless communication. Having demonstrated the potential to greatly improve transportation mobility efficiency, such dramatically increased connectivity also opens a new door for cyber attacks. In this work, we perform the first detailed security analysis of the nextgeneration CV-based transportation systems. As a first step, we target the USDOT (U.S. Department of Transportation) sponsored CV-based traffic control system, which has been tested and shown high effectiveness in real road intersections. In the analysis, we target a realistic threat, namely CV data spoofing from one single attack vehicle, with the attack goal of creating traffic congestion.We first analyze the system design and identify data spoofing strategies that can potentially influence the traffic control. Based on the strategies, we perform vulnerability analysis by exhaustively trying all the data spoofing options for these strategies to understand the upper bound of the attack effectiveness. For the highly effective cases, we analyze the causes and find that the current signal control algorithm design and implementation choices are highly vulnerable to data spoofing attacks from even a single attack vehicle. These vulnerabilities can be exploited to completely reverse the benefit of the CV-based signal control system by causing the traffic mobility to be 23.4% worse than that without adopting such system. We then construct practical exploits and evaluate them under real-world intersection settings. The evaluation results are consistent with our vulnerability analysis, and we find that the attacks can even cause a blocking effect to jam an entire approach. In the jamming period, 22% of the vehicles need to spend over 7 minutes for an original halfminute trip, which is 14 times higher. We also discuss defense directions leveraging the insights from our analysis.

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Section: Related Workmentioning

confidence: 99%

Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control

Chen

Yu-cheng²,

Feng

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

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“…For these two movements, the connector capacity constraints are given by y t ij ≤ Q ij α t ij and y t pq ≤ Q pq α t pq . For the case where both (i, j) and (p, q) have the same (single-lane) flow capacity: Q ij = Q pq = 1 vehicle per t, these constraints can alternatively be written as y t ij + y t pq ≤ 1 (10) and y t ij , y t pq ∈ {0, 1}. In order to represent this graphically, we introduce two transshipment nodes, u and v as depicted in Fig.…”

Section: ) Intersection Conflicting Movementsmentioning

confidence: 99%

Noticeability Versus Impact in Traffic Signal Tampering

Thodi¹,

Mulumba²,

Jabari

2020

IEEE Access

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This paper investigates the vulnerability of urban traffic networks to cyber attacks on traffic lights. We model traffic signal tampering as a bi-objective optimization problem that simultaneously seeks to reduce vehicular throughput in the network over time (maximize impact) while introducing minimal changes to network signal timings (minimize noticeability). We represent the Spatio-temporal traffic dynamics as a static network flow problem on a time-expanded graph. This allows us to reduce the (non-convex) attack problem to a tractable form, which can be solved using traditional techniques used to solve linear network programming problems. We show that minor but objective adjustments in the signal timings over time can severely impact traffic conditions at the network level. We investigate network vulnerability by examining the concavity of the Pareto-optimal frontier obtained by solving the bi-objective attack problem. Numerical experiments are carried to illustrate the types of insights that can be extracted from the Pareto-optimal frontier. For instance, our experiments suggest that the vulnerability of a traffic network to signal tampering is independent of the demand levels. INDEX TERMS Network vulnerability, signal tampering, traffic flow dynamics, urban traffic networks, adversarial attacks, cybersecurity.

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“…Works ( Ghena et al, 2014;Cerrudo, 2015 ) have demonstrated that networked traffic systems are vulnerable to a variety of cyber attacks. Other works ( Ghafouri et al, 2016;Laszka et al, 2016;Reilly et al, 1755;Chen et al, 2018;Shoukry et al, 2018 ) attempt to quantify cyber-physical impacts of traffic control cyber-attacks and also attempt to solve for optimal attacks. Shoukry et al (2018) also tries to design a traffic network that is resilient to Sybil (fake car) attacks.…”

Section: Motivationmentioning

confidence: 99%

“…To model similar attacks to the one in the motivational example, related works ( Chen et al, 2018;Ghafouri et al, 2016;Laszka et al, 2016 ) have implemented existing traffic network and behavior models such as the Cell Transmission Model (CTM) ( Daganzo, 1995 ) and the single queue model-based network model ( Varaiya, 2013 ). Still, most of these models contain limited analytical properties, miss certain behaviors, and are challenging to use for larger grid networks (e.g., Manhattan grid networks).…”

Section: Research Challenges and Contributionsmentioning

confidence: 99%

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Security analysis for fixed-time traffic control systems

Lopez

Jin

Faruque

2020

Transportation Research Part B: Methodological

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Wireless communication is being used as an enabling technology with traditional fixed traffic control systems in this transitional era toward Intelligent Transportation Systems (ITS). Unfortunately, major security concerns have arisen with respect to ever-increasing complexity and interconnectivity, and a noticeable lack of attention for security in these systems. Addressing concerns is a colossal challenge as it requires thorough development and formal analysis of a system model with respect to security. To tackle this challenge, we present a novel formal attack modeling and impact analysis methodology based on the Link Queue Model (LQM) of traffic flow inside a double ring road network, which is equivalent to a grid network with homogeneous links. We develop attack models as functions of tampered traffic control settings (e.g., green time ratios, cycle length, retaining ratios) with outputs equivalent to mobility impacts on the traffic network (e.g., time until system reaches state convergence, asymptotic average network flow). Further, for a given attack model, we define and identify vulnerable states: states that are critical to protect because they lead to negative impacts under the given attack model. Using our methodology we found that for certain vulnerable states, after only a few cycles of tampered control settings an attacker could cause a real impact of 1.5x speed-up in gridlock state convergence or 37%-99% drop in the asymptotic average flow rate. These results imply potentially drastic financial costs for cities and all involved drivers if similar attacks were performed on a real traffic control system.

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Vulnerability of fixed-time control of signalized intersections to cyber-tampering

Cited by 17 publications

References 11 publications

Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control

Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control

Noticeability Versus Impact in Traffic Signal Tampering

Security analysis for fixed-time traffic control systems

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