A Mechanism to Localize, Detect, and Prevent Jamming in Connected and Autonomous Vehicles (CAVs)

One of the challenges in Connected and Autonomous Vehicles (CAVs) and Vehicular Ad Hoc Networks (VANETs) is jamming attacks. These attacks present safety concerns and may render the whole communication network ineffective. Designing an anti-jamming solution for CAVs and VANETs is non-trivial because every jamming scenario presents itself with unique characteristics. In this paper, the authors extend their previous work in Alam (2021) by proposing a scheme that localizes jamming vehicles, maps the jammed area, and prevents the jamming vehicles from sending and/or receiving beacons in the network. To achieve these objectives, they apply Centroid Localization (CL), Graham Scan Hull Algorithm (GSHA), and bloom filter data structure. The novelties of their work are three-fold. 1) The authors dynamically change the X, Y positions and radii of the jamming vehicles. 2) They implement multiple jamming attack scenarios with multiple jammers and varying number of vehicles. 3) They prevent the jamming vehicles from sending messages in the network. Simulation results show that on average, their model achieves an accuracy of 97.77%, a precision of 99.14%, a recall of 93.54%, and an F1 score of 96.03% for all the attack scenarios implemented.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2023.3314737
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Alam, Md Shah
  • Oluoch, Jared
  • Kim, Junghwan
• Publication Date: 2024-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1215-1224
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Computer security; Connected vehicles; Mapping; Radio frequency interference; Vehicular ad hoc networks
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01913498
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 1 2024 9:14AM