Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Modeling and detecting false data injection attacks against railway traction power systems

Tools
- Tools
+ Tools

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.

[img]
Preview
PDF
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

Request Changes to record.

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:
DateEvent
September 2018Published
21 September 2017Accepted
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:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDNational Research Foundationhttp://dx.doi.org/10.13039/100011512
NRF2014NCR-NCR001-31National Cybersecurity R&D DirectorateUNSPECIFIED

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us