Connected vehicle (CV) technologies enable data exchange between vehicles and transportation infrastructure. In a CV environment, traffic signal control systems receive CV trajectory data through vehicle-to-infrastructure (V2I) communications to make control decisions. Comparing with existing data collection methods (e.g., from loop-detectors), the CV trajectory data provide much richer information, and therefore have great potentials to improve the system performance by reducing total vehicle delay at signalized intersections. However, this connectivity might also bring cyber security concerns.

In this paper, we aim to investigate the security problem of CV-based traffic signal control (CV-TSC) systems. Specifically, we focus on evaluating the impact of falsified data attacks on the system performance. A black-box attack scenario, in which the control logic of a CV-TSC system is unavailable to attackers, is considered. A two-step attack model is constructed. In the first step, the attacker tries to learn the control logic using a surrogate model. Based on the surrogate model, in the second step, the attacker launches falsified data attacks to influence the control systems to make sub-optimal control decisions. In the case study, we apply the attack model to an existing CV-TSC system (i.e., I-SIG) and find intersection delay can be significantly increased. Finally, we discuss some promising defense directions.

View More Papers

MUVIDS: False MAVLink Injection Attack Detection in Communication for...

Seonghoon Jeong, Eunji Park, Kang Uk Seo, Jeong Do Yoo, and Huy Kang Kim (Korea University)

Read More

Scenario-Driven Assessment of Cyber Risk Perception at the Security...

Simon Parkin (TU Delft), Kristen Kuhn, Siraj Ahmed Shaikh (Coventry University)

Read More

Differentially Private Health Tokens for Estimating COVID-19 Risk

David Butler, Chris Hicks, James Bell, Carsten Maple, and Jon Crowcroft (The Alan Turing Institute)

Read More

Доверя́й, но проверя́й: SFI safety for native-compiled Wasm

Evan Johnson (University of California San Diego), David Thien (University of California San Diego), Yousef Alhessi (University of California San...

Read More