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Cyber-Phys. Syst."],"published-print":{"date-parts":[[2018,10,31]]},"abstract":"<jats:p>\n            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\u2014which we call\n            <jats:italic>efficiency attack<\/jats:italic>\n            and\n            <jats:italic>safety attack\u2014<\/jats:italic>\n            that (i) maximize the system\u2019s total power consumption and (ii) mislead trains\u2019 local voltages to exceed given safety-critical thresholds, respectively. To counteract, we develop a Global Attack Detection (GAD) system that serializes a\n            <jats:italic>bad data detector<\/jats:italic>\n            and a novel\n            <jats:italic>secondary attack detector<\/jats:italic>\n            designed based on unique TPS characteristics. With intact position data of trains, our detection system can effectively detect FDI attacks on trains\u2019 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.\n          <\/jats:p>","DOI":"10.1145\/3226030","type":"journal-article","created":{"date-parts":[[2018,8,21]],"date-time":"2018-08-21T12:09:47Z","timestamp":1534853387000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems"],"prefix":"10.1145","volume":"2","author":[{"given":"Subhash","family":"Lakshminarayana","sequence":"first","affiliation":[{"name":"Advanced Digital Sciences Center, Illinois at Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teo Zhan","family":"Teng","sequence":"additional","affiliation":[{"name":"GovTech, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Tan","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David K. 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