Synthetic Attack Evaluation of Anomaly Based Intrusion Detection System for Formula 1 Telemetry

Daly, Niamh (2025) Synthetic Attack Evaluation of Anomaly Based Intrusion Detection System for Formula 1 Telemetry. Masters thesis, Dublin, National College of Ireland.

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Abstract

Telemetry in Formula 1 provides the foundation for both race strategy and driver safety, transmitting thousands of data points per second from cars to engineers in real time. While essential, these data streams also represent a potential attack surface for adversaries capable of intercepting, manipulating, or delaying telemetry. Malicious tampering could mask critical safety issues such as brake overheating or tyre degradation, or mislead teams into making flawed strategic decisions. Despite the importance of telemetry to modern motorsport, there has been limited academic attention to its cybersecurity. This project investigates how anomaly based intrusion detection systems (IDS) can be applied to Formula 1 telemetry to detect unauthorised tampering in real time. Using open source telemetry data obtained via the FastF1 library, synthetic attack scenarios were simulated, including false value injection, delayed transmissions, and packet loss. A range of detection methods were implemented, combining physics informed consistency checks with machine learning approaches such as Principal Component Analysis and Isolation Forest. This work contributes both academically, by extending cybersecurity research into the underexplored context of motorsport telemetry, and practically, by outlining a framework for integrating IDS into Formula 1 operations.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Spelman, Ross UNSPECIFIED
Uncontrolled Keywords:	Anomaly Detection; Machine Learning; Formula 1; Cyber Physical Systems (CPS); IDS
Subjects:	Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2026 10:58
Last Modified:	03 Sep 2026 10:58
URI:	https://norma.ncirl.ie/id/eprint/9797

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