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

TL;DR

This paper presents a novel vehicle attack detection framework combining Unscented Kalman Filter with machine learning to improve accuracy without detailed vehicle models, validated through extensive simulations under sensor attack scenarios.

Contribution

It introduces a new integration of UKF with machine learning for vehicle intrusion detection, addressing uncertainties in vehicle dynamics without requiring detailed models.

Findings

Effective detection of sensor and actuator attacks in vehicle systems.

Enhanced adaptability and accuracy in attack detection.

Validated through extensive simulation experiments.

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.