Explainable Machine Learning for Cyberattack Identification from Traffic Flows
This work addresses cyberattack detection for transportation agencies using accessible traffic flow data, though it is incremental in applying existing XAI methods to a specific domain.
The study tackled cyberattack identification in traffic management systems by developing a deep learning-based anomaly detection system using traffic flow data, achieving improved detection accuracy and trustworthiness through Explainable AI techniques.
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.