Lightweight Detection of Abnormal Behavior Based on Semantic Features of In-Vehicle Networks

Security has been an increasing problem for intelligent connected vehicles (ICVs). Abnormal behavior of in-vehicle networks is the key for threat identification and risk prevention of ICVs. Most of the abnormal behaviors are statistical features reflecting attack models. These features are simple and easy to extract and use for lightweight detection of abnormal data. In the detection process, most attacks are detected with as lightweight overhead as possible to detect abnormal behavior for in-vehicle networks. Aiming at the abnormal behavior features of ICVs for different attacks, this paper proposes an abnormal behavior detection algorithm of in-vehicle networks based on semi-supervised learning. The authors label the abnormal data and test the unlabeled data to verify the detection performance of the proposed method, and also present the possibility of detection for unknown attacks.

• Record URL:
  • https://doi.org/10.1061/9780784485040.011
• Record URL:
  • https://ascelibrary.org/doi/10.1061/9780784485040.011
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784485040
• Supplemental Notes:
  • © 2023 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Ji, Haojie
  • Wang, Liyong
  • Zhou, Bin
  • Wang, Jingyan
  • Fang, Junzhe
• Conference:
  • 23rd COTA International Conference of Transportation Professionals
  • Location: Beijing , China
  • Date: 2023-7-14 to 2023-7-17
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 107-117
• Monograph Title: CICTP 2023: Emerging Data-Driven Sustainable Technological Innovation in Transportation

Subject/Index Terms

• TRT Terms: Computer security; Connected vehicles; Detection and identification system applications; Intelligent vehicles; Threats; Vehicular ad hoc networks
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Security and Emergencies; Vehicles and Equipment;

Filing Info

• Accession Number: 01910211
• Record Type: Publication
• ISBN: 9780784485040
• Files: TRIS, ASCE
• Created Date: Feb 27 2024 4:40PM