Cyberattack Resilience in Cooperative Driving Automation Using Experimental Data and Federated Agents: Phase II
Cooperative driving automation or Connected and Automated Vehicles (CAVs) are rapidly taking over modern intelligent transportation systems. The proliferation of CAVs has also intensified concerns around cybersecurity and data privacy. The added communication involved in these driving maneuvers serves as a vulnerable attack surface. The data communicated in Basic Safety Messages (BSMs) of CAVs is highly safety-critical, thus requires secure processing and sharing. Traditional security strategies are mostly machine learning-based that rely on centralized data processing and storage. The centralized servers act as single-point-of-trust, which is vulnerable to failure, compromising data privacy, and adding overhead to communication. To address these challenges, Federated Learning (FL) has emerged as a distributed learning paradigm that enables CAV agents to locally train models and only share model parameters with a global server for updates. This eliminates the need for raw data sharing, which preserves the privacy of sensitive data transfer during CAV communication and reduces the risk of single-point failure. Despite the benefits of FL, it is still susceptible to threats like poisoning attacks, inference-based adversaries and model manipulation. The model parameters are not secured while shared iteratively between local and global agents. It is possible for adversaries to deliberately inject anomalies into the local model updates, thereby degrading the accuracy of the global model or compromise the individual local agents. To mitigate this inherent problem of FL, Blockchain serves as the apt solution. Blockchain technology is lightweight, fully decentralized data storage framework that replaces conventional centralized databases by providing immutability and tamper-proofing to the stored data. The Secure Hashing Algorithm (SHA) and smart contracts employed by Blockchains facilitate trust and accountability in this storage solution. This research will integrate blockchain with FL to secure the training data shared between FL’s distributed agents. Due to the distributed nature of both frameworks, they complement each other well and are completely compatible for integration.
Language
- English
Project
- Status: Active
- Funding: $166,789.00
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Contract Numbers:
69A3552344812
69A3552348317
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 1700 E. Cold Spring Lane
Baltimore, MD 21251, Maryland United States 21251University of California, Santa Cruz
1156 High Street, Mail Stop SOE2
Santa Cruz, California United Kingdom 95064 -
Managing Organizations:
National Center for Transportation Cybersecurity and Resiliency (TraCR)
Clemson University
Clemson, SC United States 1700 E. Cold Spring Lane
Baltimore, MD 21251, Maryland United States 21251 -
Project Managers:
Chowdhury, Mashrur
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Performing Organizations:
1700 E. Cold Spring Lane
Baltimore, MD 21251, Maryland United States 21251University of California, Santa Cruz
1156 High Street, Mail Stop SOE2
Santa Cruz, California United Kingdom 95064 -
Principal Investigators:
Khattak, Zulqarnain H
Cardenas, Alvaro
Ali, Amjad
- Start Date: 20260401
- Expected Completion Date: 20270331
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Blockchains; Computer security; Connected vehicles; Cyberattacks; Machine learning; Vehicle to vehicle communications
- Subject Areas: Data and Information Technology; Highways; Security and Emergencies; Vehicles and Equipment;
Filing Info
- Accession Number: 01988092
- Record Type: Research project
- Source Agency: National Center for Transportation Cybersecurity and Resiliency (TraCR)
- Contract Numbers: 69A3552344812, 69A3552348317
- Files: UTC, RIP
- Created Date: Apr 28 2026 4:15PM