|Title||Vulnerability of Transportation Networks to Traffic-Signal Tampering|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Laszka, A.., B.. Potteiger, Y.. Vorobeychik, S.. Amin, and X.. Koutsoukos|
|Conference Name||2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)|
|Keywords||computational complexity, Computational modeling, Internet, Merging, microsimulation model, NP-hard problem, Numerical models, polynomial-time heuristic algorithm, Roads, Schedules, security of data, signal configuration, SUMO traffic simulator, traffic engineering computing, traffic-signal tampering, Transportation, transportation network vulnerability, Vehicles|
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.