A Sensor Fusion-Based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
| Autor: | Sagar Dasgupta, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Cryptography and Security Mechanical Engineering Automotive Engineering FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Cryptography and Security (cs.CR) Machine Learning (cs.LG) Computer Science Applications |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 23:23559-23572 |
| ISSN: | 1558-0016 1524-9050 |
| DOI: | 10.1109/tits.2022.3197817 |
| Popis: | This paper presents a sensor fusion based Global Navigation Satellite System (GNSS) spoofing attack detection framework for autonomous vehicles (AV) that consists of two concurrent strategies: (i) detection of vehicle state using predicted location shift -- i.e., distance traveled between two consecutive timestamps -- and monitoring of vehicle motion state -- i.e., standstill/ in motion; and (ii) detection and classification of turns (i.e., left or right). 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. This location shift is then compared with the GNSS-based location shift to detect an attack. We have then combined k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect and classify left and right turns using data from the steering angle sensor. To prove the efficacy of the sensor fusion-based attack detection framework, attack datasets are created for four unique and sophisticated spoofing attacks-turn-by-turn, overshoot, wrong turn, 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 four types of spoofing attacks within the required computational latency threshold. arXiv admin note: substantial text overlap with arXiv:2106.02982 |
| Databáze: | OpenAIRE |
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