Developing Secure Strategies for Vehicular Ad hoc Networks in Connected and Autonomous Vehicles
The research aims at developing a resilient framework to be applied to transportation systems using connected and autonomous vehicles (CAVs). This innovation potentially responds to accident rates often related to inefficient communication systems, supported by a variety of state-of-the-art safety applications. A Vehicular Ad hoc Network (VANET) is a self-organized, multi-purpose, service-oriented communication network enabling vehicle-to-vehicle and vehicle-to-roadside infrastructure communication for the purpose of exchanging messages to ensure an efficient and comfortable traffic system on roads. Its value, however, can potentially be impaired by cyberattacks. In particular, the focus of this research will be on false data injection attacks, in which a malicious agent aims at affecting CAV behavior by injecting in the network false information concerning, for example, the traffic condition in the area or the availability of charging stations for the CAVs. Computational and analytical frameworks are developed to assess cyber risks of connected and autonomous vehicles (CAVs) and develop detection techniques based on learning algorithms. Countermeasures are then developed using anomaly identification techniques based on the learning and detection algorithms.
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Supplemental Notes:
- This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program. Supporting datasets available at: https://doi.org/10.5281/zenodo.4310277; https://rosap.ntl.bts.gov/view/dot/61931
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Corporate Authors:
C2SMART Connected Cities with Smart Transportation
NYU Tandon School of Engineering
Department of Civil and Urban Engineering
Brooklyn, NY United StatesOffice of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Authors:
- Zhu, Quanyan
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0000-0002-0008-2953
- Zimmerman, Rae
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0000-0001-5825-3383
- Fang, Song
- Zhou, Siyu
- Publication Date: 2020-5
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 39p
Subject/Index Terms
- TRT Terms: Algorithms; Autonomous vehicles; Computer security; Connected vehicles; Countermeasures; Mobile communication systems; Risk assessment; Vehicular ad hoc networks
- Subject Areas: Data and Information Technology; Highways; Security and Emergencies; Vehicles and Equipment;
Filing Info
- Accession Number: 01766490
- Record Type: Publication
- Files: UTC, NTL, TRIS, ATRI, USDOT
- Created Date: Mar 8 2021 11:42AM