DeepPOSE:Detecting GPS spoofing attack via deep recurrent neural network

作     者：Peng Jiang Hongyi Wu Chunsheng Xin 

作者机构：ECE Dept.and School of CybersecurityOld Dominion UniversityNorfolkVA23529USA 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2022年第8卷第5期

页      面：791-803页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported in part by NSF under Grants CNS-1950704 CNS-1828593 and OAC-1829771 ONR under Grant N00014-20-1-2065 NSA under Grant H98230-21-1-0278 and the Commonwealth Cyber Initiative 

主　　题：GPS spoofing attack Position estimation Recurrent neural network 

摘      要：The Global Positioning System(GPS)has become a foundation for most location-based services and navigation systems,such as autonomous vehicles,drones,ships,and wearable ***,it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing *** tools,such as Fake GPS,Lockito,and software-defined radio,enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being ***,it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion *** this end,we propose DeepPOSE,a deep learning model,to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile *** design uses a convolutional and recurrent neural network to reduce the noise,to recover a vehicle s real-time trajectory from multiple sensor *** further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map,to smartly eliminate the accumulation of errors on the trajectory *** reconstructed trajectory from sensors is then used to detect the GPS spoofing *** with the existing method,the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks.