Modeling and detecting false data injection attacks against railway traction power systems
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2019
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| Online Access: | https://hdl.handle.net/10356/85485 http://hdl.handle.net/10220/50124 |
| _version_ | 1811678026701733888 |
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| author | Lakshminarayana, Subhash Teng, Teo Zhan Tan, Rui Yau, David K. Y. |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Lakshminarayana, Subhash Teng, Teo Zhan Tan, Rui Yau, David K. Y. |
| author_sort | Lakshminarayana, Subhash |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:46:43Z |
| format | Journal Article |
| id | ntu-10356/85485 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:46:43Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/854852020-03-07T11:48:55Z Modeling and detecting false data injection attacks against railway traction power systems Lakshminarayana, Subhash Teng, Teo Zhan Tan, Rui Yau, David K. Y. School of Computer Science and Engineering Railway Traction Power Systems False Data Injection Attacks Engineering::Computer science and engineering 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. NRF (Natl Research Foundation, S’pore) Accepted version 2019-10-10T04:11:57Z 2019-12-06T16:04:39Z 2019-10-10T04:11:57Z 2019-12-06T16:04:39Z 2018 Journal Article Lakshminarayana, S., Teng, T. Z., Tan, R., & Yau, D. K. Y. (2018). Modeling and detecting false data injection attacks against railway traction power systems. ACM Transactions on Cyber-Physical Systems, 2(4), 1-29. doi:10.1145/3226030 2378-962X https://hdl.handle.net/10356/85485 http://hdl.handle.net/10220/50124 10.1145/3226030 en ACM Transactions on Cyber-Physical Systems © 2018 ACM. All rights reserved. This paper was published in ACM Transactions on Cyber-Physical Systems and is made available with permission of ACM. 30 p. application/pdf |
| spellingShingle | Railway Traction Power Systems False Data Injection Attacks Engineering::Computer science and engineering Lakshminarayana, Subhash Teng, Teo Zhan Tan, Rui Yau, David K. Y. Modeling and detecting false data injection attacks against railway traction power systems |
| title | Modeling and detecting false data injection attacks against railway traction power systems |
| title_full | Modeling and detecting false data injection attacks against railway traction power systems |
| title_fullStr | Modeling and detecting false data injection attacks against railway traction power systems |
| title_full_unstemmed | Modeling and detecting false data injection attacks against railway traction power systems |
| title_short | Modeling and detecting false data injection attacks against railway traction power systems |
| title_sort | modeling and detecting false data injection attacks against railway traction power systems |
| topic | Railway Traction Power Systems False Data Injection Attacks Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/85485 http://hdl.handle.net/10220/50124 |
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