Cybersecurity Solutions

Our group develops methods to protect next-generation transportation systems from cyber threats and sensor failures. We design real-time anomaly detection algorithms, robust control strategies, and learning frameworks that safeguard connected and automated vehicles and supporting infrastructure. These advances provide theoretical and computational foundations for resilient, data-driven mobility systems capable of sensing, learning, and adapting in the presence of uncertainty and adversarial behavior.

Key publications Example projects

Technical approach

Machine learning, Filters, Bayesian optimization, Reinforcement learning, Bandits, Graph-based modeling, Robust and stochastic optimization, Probabilistic inference, Deep learning (including autoencoders and generative models), and Game-theoretic analysis.

Example projects

Balancing Safety, Cyber-Security, and Mobility: Quantifying the Impact of Sensor Redundancy in Connected and Automated Vehicles
Road-Side Based Cybersecurity in Connected and Automated Vehicle Systems

Key publications

Rezaei, S., Masoud, N., & Khojandi, A. (2024). GAAD: GAN-enabled autoencoder for real-time sensor anomaly detection and recovery in autonomous driving . IEEE Sensors Journal, 24(7), 11734-11742.
Wang, Y., Zhang, R., Masoud, N., & Liu, H. X. (2023). Anomaly detection and string stability analysis in connected automated vehicular platoons . Transportation Research Part C: Emerging Technologies, 151, 104114.
Watts, J., Van Wyk, F., Rezaei, S., Wang, Y., Masoud, N., & Khojandi, A. (2022). A dynamic deep reinforcement learning-Bayesian framework for anomaly detection . IEEE Transactions on Intelligent Transportation Systems, 23(12), 22884-22894.
Wang, Y., & Masoud, N. (2021). Adversarial Online Learning with Variable Plays in the Pursuit-Evasion Game: Theoretical Foundations and Application in Connected and Automated Vehicle Cybersecurity . IEEE Access, 9, 142475-142488.
Wang, Y., Masoud, N., & Khojandi, A. (2020). Real-time sensor anomaly detection and recovery in connected automated vehicle sensors . IEEE Transactions on Intelligent Transportation Systems, 22(3), 1411-1421.
Wang, Y., Masoud, N., & Khojandi, A. (2020). Anomaly detection in connected and automated vehicles using an augmented state formulation . In 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), 156-161. IEEE.
Van Wyk, F., Wang, Y., Khojandi, A., & Masoud, N. (2019). Real-time sensor anomaly detection and identification in automated vehicles . IEEE Transactions on Intelligent Transportation Systems, 21(3), 1264-1276.