Vulnerability of Transportation Networks to Traffic-Signal Tampering

Abstract

Traffic signals were originally standalone hardware devices running on fixed schedules, but by now, they have evolved into complex networked systems. As a consequence, traffic signals have become susceptible to attacks through wireless interfaces or even remote attacks through the Internet. Indeed, recent studies have shown that many traffic lights deployed in practice have easily exploitable vulnerabilities, which allow an attacker to tamper with the configuration of the signal. Due to hardware-based failsafes, these vulnerabilities cannot be used to cause accidents. However, they may be used to cause disastrous traffic congestions. Building on Daganzo's well-known traffic model, we introduce an approach for evaluating vulnerabilities of transportation networks, identifying traffic signals that have the greatest impact on congestion and which, therefore, make natural targets for attacks. While we prove that finding an attack that maximally impacts congestion is NP-hard, we also exhibit a polynomial-time heuristic algorithm for computing approximately optimal attacks. We then use numerical experiments to show that our algorithm is extremely efficient in practice. Finally, we also evaluate our approach using the SUMO traffic simulator with a real-world transportation network, demonstrating vulnerabilities of this network. These simulation results extend the numerical experiments by showing that our algorithm is extremely efficient in a microsimulation model as well.

Exposing Congestion Attack on Emerging Connected Vehicle based Traffic Signal Control

“…Prior to our study, very few studies explored the security problems in the traffic control algorithms. 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].…”

Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening

“…2) Transportation Network: We use the Grid model with Random Edges (GRE) to generate a random network topology [10], which closely resembles real-world transportation networks. 6 For a detailed description of this model, we refer the reader to [10], [11]. We use Daganzo's cell transmission model to simulate traffic flowing through the generated network [8], computing the turn decisions of the vehicles based on a linear program that minimizes total travel time [12].…”

“…https://www.epa.gov/water-research/epanet4 We set these to zero to model existing deployment since we are interested in how to invest in improving security and resilience 5. We set this to zero so that there always exists a feasible deployment 6. We instantiated the model with W = 5, L = 5, p = 0.507, and q = 0.2761 based on[10].…”

Demo Abstract: SURE: An Experimentation and Evaluation Testbed for CPS Security and Resilience

“…Specific examples that have been already developed include: (1) Resilient observation selection where a Gaussian process regression model is used to determine sensor locations that are resilient to DoS attacks [4] and (2) resilient traffic signal configuration to minimize the congestion impact of tampering attacks [5]. Fig.…”