Quantum Annealing-based Optimal Identification of Vulnerable Software Components in Connected and Autonomous Vehicles
Automotive software is a critical component of a connected and autonomous vehicle (CAV) and is responsible for the safe operation of the vehicle. It interacts with various sensors including lidar, radar, cameras, and Global Positioning System (GPS), and executes complex algorithms to generate commands for the actuators such as steering wheel, brake, and gas pedals. The complexity of automotive software has grown over the last few years and is expected to grow exponentially in the next few years. The accelerated growth is fueled by factors including the integration of advanced driver assistance features, autonomous driving, and most importantly, the increased demand for electric vehicles. While improving driving comfort, and automation, such significant growth will bring immense challenges in terms of ensuring the safety and reliability of automotive software. The increase in code leads to an expanded attack surface that will allow hackers to discover vulnerable software and exploit it to launch malicious attacks against the CAV. An in-depth analysis of the attack surface, along with a meticulous identification of the most vulnerable software components/modules, can certainly help in deciding the appropriate countermeasures to avoid cyber-attacks. This project aims at developing a novel optimization model of the form Quadratic Binary unconstrained optimization (QUBO) to optimally identify the most vulnerable software modules by taking several factors (e.g., the attack surface of automotive software, downtime of modules, cost of protecting the modules, etc.,) into consideration. This project aims to investigate quantum annealing, a quantum computing technique in solving QUBO. The project team will also design classical metaheuristics: simulated Annealing (SA), and genetic algorithm as benchmarks to assess the effectiveness of the QA-based approach. The major goals of this project are to (1) analyze the attack surface of automotive software and produce interaction graphs, (2) define vulnerable S/W module identification problem and develop a QUBO model, (3) implement QUBO on a quantum annealer, (4) design SA and GA algorithms to solve the optimization problem, and (5) compare the optimal performance of QA-based approach with SA and GA-based algorithms.
Language
- English
Project
- Status: Active
- Funding: $159,054.00
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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 1600 Harden Street
Columbia, South Carolina United States 29204South Carolina State University
300 College Street NE
Orangeburg, South Carolina United States 29117 -
Managing Organizations:
National Center for Transportation Cybersecurity and Resiliency (TraCR)
Clemson University
Clemson, SC United StatesSouth Carolina State University
300 College Street NE
Orangeburg, South Carolina United States 29117 -
Project Managers:
Chowdhury, Mashrur
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Performing Organizations:
South Carolina State University
300 College Street NE
Orangeburg, South Carolina United States 29117 1600 Harden Street
Columbia, South Carolina United States 29204 -
Principal Investigators:
Sahoo, Jagruti
Mwakalonge, Judith
Swain, Nikunja
Biswal, Biswajit
Iyangar, Balaji
- Start Date: 20250101
- Expected Completion Date: 20251231
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Algorithms; Autonomous vehicles; Computer security; Connected vehicles; Risk assessment; Software
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Security and Emergencies; Vehicles and Equipment;
Filing Info
- Accession Number: 01950453
- Record Type: Research project
- Source Agency: National Center for Transportation Cybersecurity and Resiliency (TraCR)
- Contract Numbers: 69A3552344812, 69A3552348317
- Files: UTC, RIP
- Created Date: Mar 31 2025 5:22PM