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arXiv · 2505.01489

Machine Learning for Cyber-Attack Identification from Traffic Flows

Yujing Zhou·Marc L. Jacquet·Robel Dawit·Skyler Fabre·Dev Sarawat·Faheem Khan·Madison Newell·Yongxin Liu·Dahai Liu·Hongyun Chen·Jian Wang·Huihui Wang
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Abstract

This paper presents our simulation of cyber-attacks and detection strategies on the traffic control system in Daytona Beach, FL. using Raspberry Pi virtual machines and the OPNSense firewall, along with traffic dynamics from SUMO and exploitation via the Metasploit framework. We try to answer the research questions: are we able to identify cyber attacks by only analyzing traffic flow patterns. In this research, the cyber attacks are focused particularly when lights are randomly turned all green or red at busy intersections by adversarial attackers. Despite challenges stemming from imbalanced data and overlapping traffic patterns, our best model shows 85\% accuracy when detecting intrusions purely using traffic flow statistics. Key indicators for successful detection included occupancy, jam length, and halting durations.

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Yujing Zhou, Marc L. Jacquet, Robel Dawit, Skyler Fabre, Dev Sarawat, Faheem Khan, Madison Newell, Yongxin Liu, Dahai Liu, Hongyun Chen, Jian Wang, Huihui Wang. 2025-05-02. Machine Learning for Cyber-Attack Identification from Traffic Flows. https://arxiv.org/abs/2505.01489

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