Automated Vehicle Behavior Monitoring for Vulnerability Management

It is clear that autonomous vehicles will penetrate the marketplace in the next few years. It is unclear how prepared these systems will be to withstand cyber attacks that pose a serious threat to the safety of vehicle occupants and other road users. Previous studies have listed ways in which a hacker could cause harm to autonomous vehicle occupants. Other studies have demonstrated the feasibility of attacking vulnerable automotive systems. Safety threats might be mitigated if one could quickly identify attacks, but it is not at all clear that traditional cybersecurity threat detection approaches are well-suited to connected and autonomous vehicles. This project seeks to develop algorithms for identifying when a vehicle has been compromised in a cybersecurity attack, and new approaches to designing and evaluating such techniques. There is virtually no possibility of obtaining historical data of autonomous driving while under cybersecurity attack which is also predictive of future attacks, so reference data must be created. Well-motivated cyber attack models will be defined which characterize attacks on vehicle-internal data used by driver automations. Algorithm development will take place in a cycle of increasing sophistication of both cyber attack models in the form of a synthetic dataset and behavior monitoring algorithms for attack identification. For each iteration, a synthetic dataset of attacks models will be updated in an effort to fool the current algorithm. This cyclic method parallels real world scenarios in which cyber attack models become more sophisticated, resulting in the need for more robust counter methods.


  • English


  • Status: Active
  • Funding: $73229
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety through Disruption University Transportation Center

    Virginia Tech Transportation Institute
    Blacksburg, VA  United States  24060
  • Project Managers:

    Harwood, Leslie

  • Performing Organizations:

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061
  • Principal Investigators:

    Gorman, Thomas

  • Start Date: 20180110
  • Expected Completion Date: 20190109
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01658900
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center
  • Contract Numbers: 69A3551747115
  • Files: UTC, RiP
  • Created Date: Feb 2 2018 6:03PM