Transfer and Semi-supervised Learning for Cybersecurity in Transportation
This project is focused on the intrusion detection problem for transportation cybersecurity. Given a sequence of network flows between pairs of network nodes, the goal is to detect a subset of flows that represent network intrusions. The research team will focus on graph machine learning algorithms that learn structural patterns in the data for intrusion detection. However, due to the unavailability of labeled data indicating intrusions in transportation domains, the team will propose semi-supervised and transfer learning models for intrusion detection. Semi-supervised learning reduces the dependency of the model on large amounts of labeled data by leveraging unlabeled data. Moreover, transfer learning will enable labeled data from other domains to be used to detect intrusions in transportation systems. The proposed approaches will be evaluated on publicly available. They will be compared against existing baselines in terms of detection accuracy. The main findings will be summarized in at least one research paper and the final project report. Software, datasets, and metadata produced through the project will be made publicly available.
Language
- English
Project
- Status: Active
- Funding: $60,000.00
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Contract Numbers:
69A3552348332
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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 -
Managing Organizations:
Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)
University of Houston
Houston, TX United States -
Project Managers:
Zhang, Yunpeng
Kline, Robin
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Performing Organizations:
Houston, Texas
United States
University of Houston, Texas
Houston, TX United States -
Principal Investigators:
Silva, Arlei
Zhang, Yunpeng
- Start Date: 20230701
- Expected Completion Date: 20260630
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Computer security; Data analysis; Detection and identification systems; Machine learning
- Subject Areas: Data and Information Technology; Security and Emergencies; Transportation (General);
Filing Info
- Accession Number: 01953918
- Record Type: Research project
- Source Agency: Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE)
- Contract Numbers: 69A3552348332
- Files: UTC, RIP, STATEDOT
- Created Date: Apr 29 2025 4:48PM