Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach

Vehicular Ad hoc Network (VANET) is an enabling technology to provide a variety of convenient services in intelligent transportation systems, and yet vulnerable to various intrusion attacks. Intrusion detection systems (IDSs) can mitigate the security threats by detecting abnormal network behaviours. However, existing IDS solutions are limited to detect abnormal network behaviors under local sub-networks rather than the entire VANET. To address this problem, the authors utilize deep learning with generative adversarial networks and explore distributed Software-Defined Networking (SDN) to design a collaborative intrusion detection system (CIDS) for VANETs, which enables multiple SDN controllers jointly train a global intrusion detection model for the entire network without directly exchanging their sub-network flows. The authors prove the correctness of their CIDS in both IID (Independent Identically Distribution) and non-IID situations, and also evaluate its performance through both theoretical analysis and experimental evaluation on a real-world dataset. Detailed experimental results validate that the authors' CIDS is efficient and effective in intrusion detection for VANETs.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.3027390
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Shu, Jiangang
  • 0000-0002-4650-8008
  • Zhou, Lei
  • Zhang, Weizhe
  • 0000-0003-4783-876X
  • Du, Xiaojiang
  • 0000-0003-4235-9671
  • Guizani, Mohsen
  • 0000-0002-8972-8094
• Publication Date: 2021-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 4519-4530
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 7
  • 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: Automatic incident detection; Computer network protocols; Computer security; Machine learning; Vehicular ad hoc networks
• Subject Areas: Data and Information Technology; Highways; Security and Emergencies;

Filing Info

• Accession Number: 01787202
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 1 2021 9:43AM