Cybersecurity Risk Assessment in Connected Intelligent Systems for Designing Resilient Systems

Khattak, Zulqarnain; Qian, Sean; Lin, Gavin; Carnegie Mellon University; Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)

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Cybersecurity Risk Assessment in Connected Intelligent Systems for Designing Resilient Systems

2024-07-31
Datasets Available
By Khattak, Zulqarnain ; Qian, Sean ; Lin, Gavin

[PDF-1.12 MB]

English

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Creators:

Khattak, Zulqarnain ; Qian, Sean ; Lin, Gavin

Khattak, Zulqarnain ; Qian, Sean ; Lin, Gavin Less -
Corporate Creators:

Carnegie Mellon University ; Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)

Carnegie Mellon University ; Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC) Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Computer Security Connected Vehicles Disaster Resilience Intelligent Transportation Systems Risk Assessment
Resource Type:

Tech Report
Geographical Coverage:

Maryland ; United States

Maryland ; United States Less -
Edition:

Final Report
Corporate Publisher:

Carnegie Mellon University. Traffic21 Institute. Safety21 University Transportation Center (UTC)
Abstract:

Transportation operation and management systems utilize wired and wireless communications for managing roadways and are at significant risk of cyberattacks. Furthermore, perfect protection from cyberattacks is not realistic. Thus, this research proposes to focus on analyzing the vulnerability of cooperative driving relying on infrastructure-based communication using real-field experimental data collected at the Aberdeen center in Maryland. Multiple cyberattacks and sensor anomalies were emulated using conditions from field experiments to test the consequences of different types of cyberattacks. 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. The research would help agencies in monitoring the cooperative driving environment for anomalous behavior.
Format:

PDF
Funding:

69A3552344811
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha-512:c4f76c7dc16de85368d75bb1686fe9e4aa155509a3f48137111bc00b3ae03da9d3e8abd69d84d68b6e6681bf285786148ead88402d2b341ceec466e5eecc4858
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https://rosap.ntl.bts.gov/view/dot/77498/dot_77498_DS1.pdf
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Cybersecurity Risk Assessment in Connected Intelligent Systems for Designing Resilient Systems

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