A Reinforcement Learning Approach for Global Navigation Satellite System Spoofing Attack Detection in Autonomous Vehicles

A resilient positioning, navigation, and timing (PNT) system is a necessity for the robust navigation of autonomous vehicles (AVs). A global navigation satellite system (GNSS) provides satellite-based PNT services. However, a spoofer can tamper the authentic GNSS signal and could transmit wrong position information to an AV. Therefore, an AV must have the capability of real-time detection of spoofing attacks related to PNT receivers, whereby it will help the end-user (the AV in this case) to navigate safely even if the GNSS is compromised. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection method using low-cost in-vehicle sensor data. We have utilized the Honda Research Institute Driving Dataset to create attack and non-attack datasets to develop a deep RL model and have evaluated the performance of the deep RL-based attack detection model. We find that the accuracy of the deep RL model ranges from 99.99% to 100%, and the recall value is 100%. Furthermore, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection.

• Record URL:
  https://doi.org/10.1177/03611981221095509
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Sagar Dasgupta https://orcid.org/0000-0001-8491-662X © National Academy of Sciences: Transportation Research Board 2022.
• Authors:
  • Dasgupta, Sagar
  • 0000-0001-8491-662X
  • Ghosh, Tonmoy
  • 0000-0003-1460-2267
  • Rahman, Mizanur
  • 0000-0003-1128-753X
• Publication Date: 2022-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 318-330
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2676
  • Issue Number: 12
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Computer security; Detection and identification; Geographic information systems; In vehicle sensors; Machine learning; Real time information
• Identifier Terms: Global Navigation Satellite System
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01848095
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: Jun 7 2022 11:03AM