Cybersecurity Risk Assessment in Connected Intelligent Systems for Designing Resilient Systems [supporting dataset]
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2024-07-31
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Abstract:This research is based on the premise that perfect protection from cyberattacks is not realistic. Thus, the proposed research focuses on analyzing the vulnerability of cooperative driving relying on infrastructure based communication from real-field experimental data collected at the Aberdeen center in Maryland. Multiple cyberattacks including sensor anomalies, fake BSMs, replay and denial of service were emulated. Furthermore, the driving conditions from the field experiment were 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 were 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.
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. This dataset has been curated to CoreTrustSeal's curation level "C. Initial Curation." To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.11476980). NTL staff last accessed this dataset at its repository URL on 2024-09-12. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
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