Vulnerability of Transportation Networks to Traffic-Signal Tampering
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Amin_Vulnerability of transportation.pdf
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491.07 KB
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Author(s)
Laszka, Aron
Potteiger, Bradley
Vorobeychik, Yevgeniy
Amin, Saurabh
Koutsoukos, Xenofon
Date Issued
May 2016
Journal
Procceedings of the 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Laszka, Aron et al. “Vulnerability of Transportation Networks to Traffic-Signal Tampering.” 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS),11-14 April, Vienna, Austria, 2016, IEEE, 2016.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ICCPS.2016.7479122
