Cybersecurity risk assessment in connected intelligent systems for designing resilient systems

Cybersecurity refers to methods and practices designed for protection of networks, computers, programs, and data from attack, damage, or unauthorized access (1). Cybersecurity has emerged as threat in every field that relies on communications. Therefore, Transportation operation and management systems also utilize wired and wireless communications for managing roadways and are at significant risk of such cyberattacks. These systems were closed proprietary systems (isolated systems) in the past and had very limited cyber vulnerabilities. Those proprietary systems have now transformed into more open systems with increased accessibility (2) due to the emergence of network computing and reliance on emerging technologies such as internet of things (IoT), and connectivity. The National Transportation Communication for Intelligent Transportation Systems (ITS) Protocol (NTCIP) utilize center-to-center communications that rely on request-based protocols through XML messages (3). These protocols rely on the assumptions that most attacks are from the inside, and that hackers make up only a small portion of total intrusions, thus have no built in security (4). The U.S. DOT has also taken a huge initiative to develop a security credential management system (SCMS) (5)—a message security solution for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, communication dependency opens up a wide array of access points, which makes these systems vulnerable to cyberattacks and the least understood in terms of cybersecurity. This proposal is based on the premise that perfect protection from cyberattacks is not realistic. Thus, the proposed research would focus on analyzing the vulnerability of cooperative driving relying on infrastructure-based communication from a real-field experimental data collected at the Aberdeen center in Maryland. Multiple cyberattacks including sensor anomalies, fake BSMs, replay and denial of service would be emulated. Furthermore, the driving conditions from the field experiment would be emulated within a realistic simulation environment to test the consequences of different types of cyberattacks on safety effects of transportation systems and analyze crash types and severity. Long short-term memory with Gaussian mixture (LAGMM) model would be utilized to design efficient and effective anomaly detection method for accounting the temporal relations of trajectories, so that anomalous behavior can be detected in real-time and the severe consequences of cyberattack or sensor anomalies can be avoided. Ultimately, the research would develop a real-time threat-monitoring system to continuously check to see if the system is behaving as expected and degrade the system to a safe state under cyberattacks.


  • English


  • Status: Active
  • Funding: $90020
  • Contract Numbers:


  • Sponsor Organizations:

    Carnegie Mellon University

    Safety21 National UTC for Promoting Safety
    Pittsburgh, PA  United States  15213

    Office of the Assistant Secretary for Research and Technology

    University Transportation Center Program
  • Managing Organizations:

    Carnegie Mellon University

    Safety21 National UTC for Promoting Safety
    Pittsburgh, PA  United States  15213
  • Project Managers:

    Stearns, Amy

  • Performing Organizations:

    Carnegie Mellon University

  • Principal Investigators:

    Khattak, Zulqarnain

  • Start Date: 20230701
  • Expected Completion Date: 20240630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01900240
  • Record Type: Research project
  • Source Agency: Safety21
  • Contract Numbers: 69A3552344811
  • Files: UTC, RIP
  • Created Date: Nov 20 2023 8:30PM