Data-Driven Intrusion Detection in Vehicles: Integrating Unscented Kalman Filter (UKF) with Machine Learning

Shuhao Bian, Milad Farsi, Nasser Azad, Chris Hobbs

2024

Abstract

In the realm of Cyber–Physical System (CPS), accurately identifying attacks without detailed knowledge of the system’s parameters remains a major challenge. When it comes to Advanced Driver Assistance Systems (ADAS), identifying the parameters of vehicle dynamics could be impractical or prohibitively costly. To tackle this challenge, we propose a novel framework for attack detection in vehicles that effectively addresses the uncertainty in their dynamics. Our method integrates the widely used Unscented Kalman Filter (UKF), a well-known technique for nonlinear state estimation in dynamic systems, with machine learning algorithms. This combination eliminates the requirement for precise vehicle modeling in the detection process, enhancing the system’s adaptability and accuracy. To validate the efficacy and practicality of our proposed framework, we conducted extensive comparative simulations by introducing Denial of Service (DoS) attacks on the vehicle systems’ sensors and actuators.

Paper Citation

in Harvard Style

Bian S., Farsi M., Azad N. and Hobbs C. (2024). Data-Driven Intrusion Detection in Vehicles: Integrating Unscented Kalman Filter (UKF) with Machine Learning. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 714-723. DOI: 10.5220/0013063900003822

in Bibtex Style

@conference{icinco24,
author={Shuhao Bian and Milad Farsi and Nasser Azad and Chris Hobbs},
title={Data-Driven Intrusion Detection in Vehicles: Integrating Unscented Kalman Filter (UKF) with Machine Learning},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={714-723},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013063900003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Data-Driven Intrusion Detection in Vehicles: Integrating Unscented Kalman Filter (UKF) with Machine Learning
SN - 978-989-758-717-7
AU - Bian S.
AU - Farsi M.
AU - Azad N.
AU - Hobbs C.
PY - 2024
SP - 714
EP - 723
DO - 10.5220/0013063900003822
PB - SciTePress