Machine Learning for Cyber-Attack Identification from Traffic Flows
This addresses cybersecurity for urban traffic control systems, but it is incremental as it applies existing machine learning methods to a specific simulation scenario.
The paper tackled the problem of identifying cyber-attacks that manipulate traffic lights by analyzing traffic flow patterns, achieving 85% accuracy in detection using statistical indicators like occupancy and jam length.
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.