Explainable Machine Learning for Cyberattack Identification from Traffic Flows

The increasing automation of traffic management systems has made them prime targets for cyberattacks, disrupting urban mobility and public safety. Traditional network-layer defenses are often inaccessible to transportation agencies, necessitating a machine learning-based approach that relies solely on traffic flow data. In this study, we simulate cyberattacks in a semi-realistic environment, using a virtualized traffic network to analyze disruption patterns. We develop a deep learning-based anomaly detection system, demonstrating that Longest Stop Duration and Total Jam Distance are key indicators of compromised signals. To enhance interpretability, we apply Explainable AI (XAI) techniques, identifying critical decision factors and diagnosing misclassification errors. Our analysis reveals two primary challenges: transitional data inconsistencies, where mislabeled recovery-phase traffic misleads the model, and model limitations, where stealth attacks in low-traffic conditions evade detection. This work enhances AI-driven traffic security, improving both detection accuracy and trustworthiness in smart transportation systems.

@article{zhou2025_2505.01488,
  title={ Explainable Machine Learning for Cyberattack Identification from Traffic Flows },
  author={ Yujing Zhou and Marc L. Jacquet and Robel Dawit and Skyler Fabre and Dev Sarawat and Faheem Khan and Madison Newell and Yongxin Liu and Dahai Liu and Hongyun Chen and Jian Wang and Huihui Wang },
  journal={arXiv preprint arXiv:2505.01488},
  year={ 2025 }
}