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Welcome to EVTwinCyb
Evaluating Resilient State Estimation Techniques and Mitigation Strategies for Electric Vehicle Digital Twin Systems Against FDI and DoS Cyber Threats
Introduction: Introduce the topic of Electric Vehicle (EV) digital twin systems and their importance in modern transportation. Highlight the growing integration of advanced algorithms for navigation, sensors, actuators, and energy management in EVs.
Cybersecurity Challenges: Discuss the susceptibility of EV systems to cyberattacks, specifically False Data Injection (FDI) and Denial of Service (DoS) attacks. Explain how these attacks can compromise performance, privacy, and safety, leading to accidents and reduced efficiency.
Research Objectives: Describe the goal of modeling EV digital twin systems to simulate realistic observation data and attack profiles. Outline the aim to identify the most effective state estimation techniques for ensuring resilience in autonomous EV systems. Emphasize the potential impact of these developments on EV operations, communities, and society.
Proposed Framework: Introduce the comprehensive simulation framework for EV state estimation under cyberattacks. Mention the evaluation of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) techniques in both ideal and attack scenarios.
Key Contributions: Propose a realistic EV simulation incorporating dynamic models and cyberattack profiles. Evaluate the resilience of EKF, UKF, and PF against FDI and DoS attacks. Highlight the societal benefits of improved state estimation, fostering safer and more trustworthy EV systems.
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IEEE/ICACT20250208 Slide.01
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