An Effective Intrusion Detection System Based on Network Traffic and Packets for CBTC

With the application of advanced network and computer technologies, communication-based train control (CBTC) systems are facing increasingly serious security risks. Intrusion detection can help detect attacks of CBTC systems and avoid major accidents. The traditional intrusion detection systems (IDS) do not consider the characteristics of CBTC systems, so they cannot be applied to CBTC systems directly. In this paper, the authors analyze the characteristics of network data of CBTC systems and propose an IDS based on network traffic and packets to detect typical attacks of CBTC systems, such as the denial of service (DoS) and data tempering attacks. The self-organizing maps (SOM) neural networks are used to improve the density-based spatial clustering of applications with noise (DBscan) method since DBscan only can detect anomalies offline with low detection rate. By testing on a simulation platform of CBTC systems, it is verified that the designed IDS is suitable for CBTC systems for its great detection performance and real-time performance.

• Record URL:
  • https://doi.org/10.1007/978-981-15-2866-8_29
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9789811528651
• Supplemental Notes:
  • © Springer Nature Singapore Pte Ltd. 2020.
• Corporate Authors:

  Springer Singapore

  152 Beach Road
  Singapore,     189721
• Authors:
  • Chen, Xueqian
  • Bu, Bing
  • Yang, Xuetao
• Conference:
  • 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT 2019)
  • Location: Qingdao , China
  • Date: 2019-10-25 to 2019-10-27
• Publication Date: 2020-4

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 303-313
• Monograph Title: Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019: Rail Transportation System Safety and Maintenance Technologies
• Serial:
  • Lecture Notes in Electrical Engineering
  • Volume: 640
  • Publisher: Springer
  • ISSN: 1876-1100

Subject/Index Terms

• TRT Terms: Communication systems; Detection and identification systems; Neural networks; Railroad safety
• Subject Areas: Data and Information Technology; Railroads; Safety and Human Factors; Security and Emergencies;

Filing Info

• Accession Number: 01928490
• Record Type: Publication
• ISBN: 9789811528651
• Files: TRIS
• Created Date: Aug 23 2024 4:53PM