Explainable Machine Learning for Cyberattack Identification from Traffic Flows
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915271187365888 |
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| author | Zhou, Yujing Jacquet, Marc L. Dawit, Robel Fabre, Skyler Sarawat, Dev Khan, Faheem Newell, Madison Liu, Yongxin Liu, Dahai Chen, Hongyun Wang, Jian Wang, Huihui |
| author_facet | Zhou, Yujing Jacquet, Marc L. Dawit, Robel Fabre, Skyler Sarawat, Dev Khan, Faheem Newell, Madison Liu, Yongxin Liu, Dahai Chen, Hongyun Wang, Jian Wang, Huihui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable Machine Learning for Cyberattack Identification from Traffic Flows Zhou, Yujing Jacquet, Marc L. Dawit, Robel Fabre, Skyler Sarawat, Dev Khan, Faheem Newell, Madison Liu, Yongxin Liu, Dahai Chen, Hongyun Wang, Jian Wang, Huihui Machine Learning Cryptography and Security 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. |
| title | Explainable Machine Learning for Cyberattack Identification from Traffic Flows |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2505.01488 |