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Modeling and detecting false data injection attacks against railway traction power systems
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Lakshminarayana, Subhash, Teng, Teo Zhan, Tan, Rui and Yau, David K. Y. (2018) Modeling and detecting false data injection attacks against railway traction power systems. ACM Transactions on Cyber-Physical Systems, 2 (4). 28. doi:10.1145/3226030 ISSN 2378962X.
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WRAP-modeling-detecting-false-data-injection-RTP-Lakshminarayana-2018.pdf - Accepted Version - Requires a PDF viewer. Download (1416Kb) | Preview |
Official URL: http://dx.doi.org/10.1145/3226030
Abstract
Modern urban railways extensively use computerized sensing and control technologies to achieve safe, reliable, and well-timed operations. However, the use of these technologies may provide a convenient leverage to cyber-attackers who have bypassed the air gaps and aim at causing safety incidents and service disruptions. In this article, we study False Data Injection (FDI) attacks against railway Traction Power Systems (TPSes). Specifically, we analyze two types of FDI attacks on the train-borne voltage, current, and position sensor measurements—which we call efficiency attack and safety attack—that (i) maximize the system’s total power consumption and (ii) mislead trains’ local voltages to exceed given safety-critical thresholds, respectively. To counteract, we develop a Global Attack Detection (GAD) system that serializes a bad data detector and a novel secondary attack detector designed based on unique TPS characteristics. With intact position data of trains, our detection system can effectively detect FDI attacks on trains’ voltage and current measurements even if the attacker has full and accurate knowledge of the TPS, attack detection, and real-time system state. In particular, the GAD system features an adaptive mechanism that ensures low false-positive and negative rates in detecting the attacks under noisy system measurements. Extensive simulations driven by realistic running profiles of trains verify that a TPS setup is vulnerable to FDI attacks, but these attacks can be detected effectively by the proposed GAD while ensuring a low false-positive rate.
| Item Type: | Journal Article | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TF Railroad engineering and operation | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
| Library of Congress Subject Headings (LCSH): | Cyberterrorism -- Prevention, Street-railroads | |||||||||
| Journal or Publication Title: | ACM Transactions on Cyber-Physical Systems | |||||||||
| Publisher: | ACM | |||||||||
| ISSN: | 2378962X | |||||||||
| Official Date: | September 2018 | |||||||||
| Dates: |
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| Volume: | 2 | |||||||||
| Number: | 4 | |||||||||
| Article Number: | 28 | |||||||||
| DOI: | 10.1145/3226030 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | © The Authors | ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Cyber-Physical Systems, http://dx.doi.org/10.1145/3226030 | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 29 January 2019 | |||||||||
| Date of first compliant Open Access: | 30 January 2019 | |||||||||
| RIOXX Funder/Project Grant: |
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