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

• URL: http://arxiv.org/abs/2106.02982v1
• Date: Sat, 5 Jun 2021 23:02:55 GMT
• Title: Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
• Authors: Sagar Dasgupta, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury
• Abstract summary: A sensor fusion-based attack detection framework is presented that consists of three concurrent strategies for an autonomous vehicle. Data from multiple low-cost in-vehicle sensors are fused and fed into a recurrent neural network model. We have combined k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect turns using data from the steering angle sensor. Our analysis reveals that the sensor fusion-based detection framework successfully detects all three types of spoofing attacks within the required computational latency threshold.
• Score: 4.947150829838588
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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