Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85485
Title: Modeling and detecting false data injection attacks against railway traction power systems
Authors: Lakshminarayana, Subhash
Teng, Teo Zhan
Tan, Rui
Yau, David K. Y.
Keywords: Railway Traction Power Systems
False Data Injection Attacks
Engineering::Computer science and engineering
Issue Date: 2018
Source: 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
Series/Report no.: ACM Transactions on Cyber-Physical Systems
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.
URI: https://hdl.handle.net/10356/85485
http://hdl.handle.net/10220/50124
ISSN: 2378-962X
DOI: http://dx.doi.org/10.1145/3226030
Rights: © 2018 ACM. All rights reserved. This paper was published in ACM Transactions on Cyber-Physical Systems and is made available with permission of ACM.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Files in This Item:
File Description SizeFormat 
Modeling and Detecting False Data Injection Attacks.pdf1.09 MBAdobe PDFThumbnail
View/Open

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.