Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles

TL;DR

This paper presents a sensor fusion framework using machine learning techniques to detect GNSS spoofing attacks on autonomous vehicles by analyzing sensor data for location shifts, turns, and motion states, achieving high detection accuracy.

Contribution

The study introduces a novel sensor fusion-based detection framework combining LSTM, k-NN, and DTW algorithms for real-time GNSS spoofing attack detection in autonomous vehicles.

Findings

Successfully detects three types of spoofing attacks

Operates within real-time computational constraints

Effective use of low-cost sensors for attack detection

Abstract

In this study, a sensor fusion based GNSS spoofing attack detection framework is presented that consists of three concurrent strategies for an autonomous vehicle (AV): (i) prediction of location shift, (ii) detection of turns (left or right), and (iii) recognition of motion state (including standstill state). Data from multiple low-cost in-vehicle sensors (i.e., accelerometer, steering angle sensor, speed sensor, and GNSS) are fused and fed into a recurrent neural network model, which is a long short-term memory (LSTM) network for predicting the location shift, i.e., the distance that an AV travels between two consecutive timestamps. We have then combined k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect turns using data from the steering angle sensor. In addition, data from an AV's speed sensor is used to recognize the AV's motion state including the standstill state. To prove the efficacy of the sensor fusion-based attack detection framework, attack datasets are created for three unique and sophisticated spoofing attacks turn by turn, overshoot, and stop using the publicly available real-world Honda Research Institute Driving Dataset (HDD). Our analysis reveals that the sensor fusion-based detection framework successfully detects all three types of spoofing attacks within the required computational latency threshold.