Detection of False Data Injection Attack in Connected Vehicles via Cloud-based Sandboxing

Details

• Creators:
  Pisu, Pierluigi ; Comert, Gurcan ; Zhao, Chunheng
• Corporate Creators:
  Clemson University. Department of Automotive Engineering ; Benedict College
• Corporate Contributors:
  Center for Connected Multimodal Mobility, Clemson University ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  Attack Detection Autonomous Vehicles Cloud Computing False Data Injection Connected Vehicles Data Detection And Identification Technologies Particle Filters Security
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report (December 2018 –July 2020)
• Corporate Publisher:
  Center for Connected Multimodal Mobility, Clemson University
• Abstract:
  In recent years, developments in vehicle-to-everything communication (V2X) have steadily increased in applications such as platooning, collision avoidance, and routing algorithm. V2X provides vehicles with long range information regarding traffic congestion and routing, but also short and mid-range information allowing cooperative adaptive cruise control, automatic collision warnings, and others. Despite being potentially beneficial in several aspects, such interdependency poses a set of specific challenges from a safety and reliability standpoint due to the possibility of cyber-attacks aimed at influencing the behavior of the vehicles. In this project, a Cloud-based method to detect the false data injection attack on Connected Autonomous Vehicles (CAVs) is presented. The sandboxing concept utilized in this report comes from computer security and it is recast in a control framework as a way to isolate and evaluate the data exchanged by the CAVs affecting the vehicle control system. Numerical experiments are conducted to show the effectiveness of the approach using microscopic traffic simulation. Our results are summarized as follows: (i) both two proposed data fusion algorithms with different architecture are able to improve the localization results of connected and autonomous vehicles; (ii) both two proposed attack detection schemes are able to detect false data injection attacks in platooning scenario and rerouting scenario, respectively.
• Format:
  PDF
• Funding:
  69A3551747117
• Collection(s):
  University Transportation Centers Autonomous Vehicles
• Main Document Checksum:
  urn:sha-512:772c77ab0f205422c178e86d1ab1cf1dd0def5847a7e9b305a8bd5d770f5443e2fb1ca346ca264c7f3a59a7218f5958f1070732156bbcf886f1d60792703b9a4
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/60929/dot_60929_DS1.pdf
• File Type:
  [PDF - 1.13 MB ]