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:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
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Supplemental Notes:
- Sagar Dasgupta https://orcid.org/0000-0001-8491-662X © National Academy of Sciences: Transportation Research Board 2022.
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Authors:
- Dasgupta, Sagar
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0000-0001-8491-662X
- Ghosh, Tonmoy
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0000-0003-1460-2267
- Rahman, Mizanur
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0000-0003-1128-753X
- Publication Date: 2022-12
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 318-330
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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