Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (17): 62-73.DOI: 10.3778/j.issn.1002-8331.2401-0330
• Research Hotspots and Reviews • Previous Articles Next Articles
ZHOU Yalan, SONG Xiao’ou
Online:
2024-09-01
Published:
2024-08-30
周雅兰,宋晓鸥
ZHOU Yalan, SONG Xiao’ou. Overview of GNSS Spoofing Detection Using Machine Learning[J]. Computer Engineering and Applications, 2024, 60(17): 62-73.
周雅兰, 宋晓鸥. 利用机器学习的GNSS欺骗检测综述[J]. 计算机工程与应用, 2024, 60(17): 62-73.
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2401-0330
[1] JOUBERT N, REID T G R, NOBLE F. Developments in modern GNSS and its impact on autonomous vehicle architectures[C]//Proceedings of the 2020 IEEE Intelligent Vehicles Symposium, 2020: 2029-2036. [2] JING H, GAO Y, SHAHBEIGI S, et al. Integrity monitoring of GNSS/INS based positioning systems for autonomous vehicles: state-of-the-art and open challenges[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 14166-14187. [3] LYU C, ZHAN R. Global analysis of active defense technologies for unmanned aerial vehicle[J]. IEEE Aerospace and Electronic Systems Magazine, 2022, 37(1): 6-31. [4] LYU X, HU B, WANG Z, et al. A SINS/GNSS/VDM integrated navigation fault-tolerant mechanism based on adaptive information sharing factor[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-13. [5] GROVES P D, LONG D C. Combating GNSS interference with advanced inertial integration[J]. Journal of Navigation, 2005, 58(3): 419-432. [6] CELIS R D, CADARSO L. GNSS/IMU laser quadrant detector hybridization techniques for artillery rocket guidance[J]. Nonlinear Dynamics, 2018, 91(4): 2683-2698. [7] WANG S, WANG J, SU C, et al. Intelligent detection algorithm against UAVs’ GPS spoofing attack[C]//Proceedings of the 2020 IEEE 26th International Conference on Parallel and Distributed Systems, 2020: 382-389. [8] ZHU X, HUA T, YANG F, et al. Global positioning system spoofing detection based on support vector machines[J]. IET Radar, Sonar & Navigation, 2022, 16(2): 224-237. [9] BOSE S C. GPS spoofing detection by neural network machine learning[J]. IEEE Aerospace and Electronic Systems Magazine, 2022, 37(6): 18-31. [10] SIEMURI A, KUUSNIEMI H, ELMUSRATI M S, et al. Machine learning utilization in GNSS—use cases, challenges and future applications[C]//Proceedings of the 2021 International Conference on Localization and GNSS, 2021: 1-6. [11] DANG Y. Machine learning based GNSS spoo?ng detection and mitigation for cellular-connected UAVs[D]. Helsinki: Aalto University, 2023. [12] SIEMURI A, SELVAN K, KUUSNIEMI H, et al. A systematic review of machine learning techniques for GNSS use cases[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(6): 5043-5077. [13] JUNZHI L, WANQING L, QIXIANG F, et al. Research progress of GNSS spoofing and spoofing detection technology[C]//Proceedings of the 2019 IEEE 19th International Conference on Communication Technology, 2019: 1360-1369. [14] MENG L, YANG L, YANG W, et al. A survey of GNSS spoofing and anti-spoofing technology[J]. Remote Sensing, 2022, 14(19): 4826. [15] 周彦, 王山亮, 杨威, 等. GNSS欺骗式干扰检测综述[J]. 计算机工程与应用, 2022, 58(11): 12-22. ZHOU Y, WANG S L, YANG W, et al. Overview of GNSS spoofing jamming detection[J]. Computer Engineering and Applications, 2022, 58(11): 12-22. [16] 张鑫. 卫星导航欺骗干扰信号检测技术综述[J]. 全球定位系统, 2018, 43(6): 1-7. ZHANG X. Overview of satellite navigation spoofing signal detection technology[J]. GNSS World of China, 2018, 43(6): 1-7. [17] 刘清秀, 程玉, 王国栋, 等. 北斗卫星导航欺骗与抗欺骗技术现状探讨[J]. 导航与控制, 2021, 20(4): 24-32. LIU Q X, CHENG Y, WANG G D, et al. Discussion on detection and anti-deception technology of Beidou satellite navigation[J]. Navigation and Control, 2021, 20(4): 24-32. [18] BIAN S, JI B, HU Y. Research status and prospect of GNSS anti-spoofing technology[J]. Scientia Sinica Informationis, 2017, 47(3): 275-287. [19] AISSOU G, SLIMANE H O, BENOUADAH S, et al. Tree-based supervised machine learning models for detecting GPS spoofing attacks on UAS[C]//Proceedings of the 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, 2021: 649-653. [20] TALAEI KHOEI T, ISMAIL S, SHAMAILEH K A, et al. Impact of dataset and model parameters on machine learning performance for the detection of GPS spoofing attacks on unmanned aerial vehicles[J]. Applied Sciences, 2022, 13(1): 383. [21] KHOEI T T, AISSOU G, AL SHAMAILEH K, et al. Supervised deep learning models for detecting GPS spoofing attacks on unmanned aerial vehicles[C]//Proceedings of the 2023 IEEE International Conference on Electro Information Technology, 2023: 340-346. [22] AISSOU G, BENOUADAH S, EL ALAMI H, et al. Instance-based supervised machine learning models for detecting GPS spoofing attacks on UAS[C]//Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, 2022: 208-214. [23] KHOEI T T, GASIMOVA A, AHAJJAM M A, et al. A comparative analysis of supervised and unsupervised models for detecting GPS spoofing attack on UAVs[C]//Proceedings of the 2022 IEEE International Conference on Electro Information Technology, 2022: 279-284. [24] GASIMOVA A. Performance comparison of weak and strong learners in detecting GPS spoofing attacks on unmanned aerial vehicles (UAVs)[D]. Grand Forks: University of North Dakot, ?2022. [25] GASIMOVA A, KHOEI T T, KAABOUCH N. A comparative analysis of the ensemble models for detecting GPS spoofing attacks on UAVs[C]//Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, 2022: 310-315. [26] KHOEI T T, ISMAIL S, KAABOUCH N. Dynamic selection techniques for detecting GPS spoofing attacks on UAVs[J]. Sensors, 2022, 22(2): 662. [27] ESHMAWI A A, UMER M, ASHRAF I, et al. Enhanced machine learning ensemble approach for securing small unmanned aerial vehicles from GPS spoofing attacks[J]. IEEE Access, 2024, 12: 27344-27355. [28] LOHAN E S, FERRE R M, RICHTER P, et al. GNSS navigation threats management on-board of aircraft[J]. INCAS Bulletin, 2019, 11(3): 111-125. [29] HUMPHREYS T, BHATTI J, SHEPARD D, et al. The texas spoofing test battery: toward a standard for evaluating GPS signal authentication techniques[J]. Proceedings of the 25th International Technical Meeting of the Satellite Division of the Institute of Navigation, 2012: 3569-3583. [30] HUMPHREYS T. TEXBAT data sets 7 and 8[EB/OL]. (2016-02-26)[2024-01-29]. https://rnl-data.ae.utexas.edu/datastore/texbat/texbat_ds7_and_ds8.pdf. [31] ALBRIGHT A, POWERS S, BONIOR J, et al. A tool for furthering GNSS security research: the oak ridge spoofing and interference test battery[C]//Proceedings of the 33rd International Technical Meeting of the Satellite Division of the Institute of Navigation, 2020: 3697-3712. [32] ISLAM S, BHUIYAN M Z H, LIAQUAT M, et al. An open GNSS spoofing data repository: characterization and impact analysis with FGI-GSRx open-source software-defined receiver[EB/OL]. (2024-03-12)[2024-04-09]. https://www.researchsquare.com/article/rs-4021306/v1. [33] WANG X, YANG J, HUANG M, et al. GNSS interference and spoofing dataset[J]. Data in Brief, 2024, 54: 110302-110316. [34] CALVO-PALOMINO R, BHATTACHARYA A, BOVET G, et al. Short: LSTM-based GNSS spoofing detection using low-cost spectrum sensors[C]//Proceedings of the 2020 IEEE 21st International Symposium on “A World of Wireless, Mobile and Multimedia Networks”, 2020: 273-276. [35] SUN Y, YU M, WANG L, et al. A deep-learning-based GPS signal spoofing detection method for small UAVs[J]. Drones, 2023, 7(6): 370. [36] NAYFEH M, LI Y, SHAMAILEH K A, et al. Machine learning modeling of GPS features with applications to UAV location spoofing detection and classification[J]. Computers & Security, 2023, 126: 103085. [37] ALAMI E H, HALL K, RAWAT D B. Comparative study of machine learning techniques for detecting GPS spoofing attacks on mission critical military IoT devices[C]//Proceedings of the 2023 IEEE International Conference on Communications Workshops, 2023: 512-517. [38] SEMANJSKI S, SEMANJSKI I, WILDE D W, et al. Use of supervised machine learning for GNSS signal spoofing detection with validation on real-world meaconing and spoofing data—part I[J]. Sensors, 2020, 20(4): 1171. [39] SUNG Y H, PARK S J, KIM D Y, et al. GPS spoofing detection method for small UAVs using 1D convolution neural network[J]. Sensors, 2022, 22(23): 9412. [40] FERRE M R, FUENTE D L A, LOHAN E S. Jammer classification in GNSS bands via machine learning algorithms[J]. Sensors, 2019, 19(22): 4841. [41] IQBAL A, AMAN M N, SIKDAR B. A deep learning based induced GNSS spoof detection framework[J]. IEEE Transactions on Machine Learning in Communications and Networking, 2024, 2: 457-478. [42] TOHIDI S, MOSAVI M R. Effective detection of GNSS spoofing attack using a multi-layer perceptron neural network classifier trained by PSO[C]//Proceedings of the 2020 25th International Computer Conference, Computer Society of Iran, 2020: 1-5. [43] HUANG C, CHEN Z, PENG X, et al. A GNSS spoofing detection method based on CNN-DOA[C]//Proceedings of the China Satellite Navigation Conference, 2024: 402-414. [44] SHAFIEE E, MOSAVI M R, MOAZEDI M. Detection of spoofing attack using machine learning based on multi-layer neural network in single-frequency GPS receivers[J]. Journal of Navigation, 2018, 71(1): 169-188. [45] IQBAL A, AMAN M N, SIKDAR B. Machine learning based time synchronization attack detection for synchrophasors[C]//Proceedings of the 2023 IEEE Global Communications Conference, 2023: 2251-2256. [46] BORHANI-DARIAN P, LI H, WU P, et al. Deep neural network approach to detect GNSS spoofing attacks[C]//Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation, 2020: 3241-325. [47] LI J, ZHU X, OUYANG M, et al. GNSS spoofing jamming detection based on generative adversarial network[J]. IEEE Sensors Journal, 2021, 21(20): 22823. [48] ZARRINNEGAR K, TOHIDI S, MOSAVI M R, et al. Improving cross ambiguity function using image processing approach to detect GPS spoofing attacks[J]. Iranian Journal of Electrical and Electronic Engineering, 2023, 19(1): 2584. [49] CHEN Z, LI J, LI J, et al. GNSS multiparameter spoofing detection method based on support vector machine[J]. IEEE Sensors Journal, 2022, 22(18): 17864-17874. [50] ZUO S, LIU Y, ZHANG D, et al. Detection of GPS spoofing attacks based on isolation forest[C]//Proceedings of the 2021 IEEE 9th International Conference on Information, Communication and Networks, 2021: 357-361. [51] SEMANJSKI S, MULS A, SEMANJSKI I, et al. Use and validation of supervised machine learning approach for detection of GNSS signal spoofing[C]//Proceedings of the 2019 International Conference on Localization and GNSS, 2019: 1-6. [52] SHAFIQUE A, MEHMOOD A, ELHADEF M. Detecting signal spoofing attack in UAVs using machine learning models[J]. IEEE Access, 2021, 9: 93803-93815. [53] LI J, LI W, HE S, et al. Research on detection of spoofing signal with small delay based on KNN[C]//Proceedings of the 2020 IEEE 3rd International Conference on Electronics Technology, 2020: 625-629. [54] PARDHASARADHI B, YAKKATI R R, CENKERAMADDI L R. Machine learning-based screening and measurement to measurement association for navigation in GNSS spoofing environment[J]. IEEE Sensors Journal, 2022, 22(23): 23423-23435. [55] QIN W, DOVIS F. Situational awareness of chirp jamming threats to GNSS based on supervised machine learning[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(3): 1707-1720. [56] LI J, CHEN Z, RAN Z, et al. The GNSS spoofing detection method based on AdaBoost[C]//Proceedings of the 2023 6th International Symposium on Autonomous Systems, 2023: 1-6. [57] GALLARDO F, YUSTE A P. SCER spoofing attacks on the Galileo open service and machine learning techniques for end-user protection[J]. IEEE Access, 2020, 8: 85515-85532. [58] WEI X, WANG Y, SUN C. PerDet: machine-learning-based UAV GPS spoofing detection using perception data[J]. Remote Sensing, 2022, 14(19): 4925. [59] ZHANG K, TUHIN R A, PAPADIMITRATOS P. Detection and exclusion RAIM algorithm against spoofing/replaying attacks[C]//Proceedings of the International Symposium on GNSS 2015, 2015: 81-90. [60] FENG Z, SEOW C K, CAO Q. GNSS anti-spoofing detection based on Gaussian mixture model machine learning[C]//Proceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems, 2022: 3334-3339. [61] IQBAL A, AMAN M N, SIKDAR B. Machine and representation learning based GNSS spoofing detectors utilizing feature set from generic GNSS receivers[J]. IEEE Transactions on Consumer Electronics, 2024, 70(1): 574-583. [62] ZHANG X, HUANG Y, TIAN Y, et al. Noise-like features-assisted GNSS spoofing detection based on convolutional autoencoder[J]. IEEE Sensors Journal, 2023, 23(20): 25473-25486. [63] BREWINGTON J, KAR D. UAV GPS spoofing detection via neural generative one-class classification[C]//Proceedings of the 24th International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, 2023: 492-497. [64] ROY D, MUKHERJEE T, RIDEN A, et al. GANSAT: a GAN and satellite constellation fingerprint-based framework for GPS spoof-detection and location estimation in GPS deprived environment[J]. IEEE Access, 2022, 10: 45485-45507. [65] JULLIAN O, OTERO B, STOJILOVI? M, et al. Deep learning detection of GPS spoofing[C]//Proceedings of the Machine Learning, Optimization, and Data Science, 2022: 527-540. [66] ABDULLAYEVA F, VALIKHANLI O. Development of a method for detecting GPS spoofing attacks on unmanned aerial vehicles[J]. Problems of Information Technology, 2022, 13(1): 3-8. [67] LI J, ZHU X, OUYANG M, et al. Research on multi-peak detection of small delay spoofing signal[J]. IEEE Access, 2020, 8: 151777-151787. [68] BORHANI-DARIAN P, LI H, WU P, et al. Detecting GNSS spoofing using deep learning[J]. EURASIP Journal on Advances in Signal Processing, 2024(1): 14. [69] MENG L, YANG L, REN S, et al. An approach of linear regression-based UAV GPS spoofing detection[J]. Wireless Communications and Mobile Computing, 2021: 1-16. [70] ZHANG G, MENG W, MA X, et al. LSTM network based spoofing detection and recognition in a GNSS receiver[C]//Proceedings of the China Satellite Navigation Conference, 2020: 613-622. [71] XUE N, NIU L, HONG X, et al. DeepSIM: GPS spoofing detection on UAVs using satellite imagery matching[C]//Proceedings of the Annual Computer Security Applications Conference, 2020: 304-319. [72] GUIZZARO C, FORMAGGIO F, TOMASIN S. GNSS spoofing attack detection by IMU measurements through a neural network[C]//Proceedings of the 2022 10th Workshop on Satellite Navigation Technology, 2022: 1-6. [73] PANICE G, LUONGO S, GIGANTE G, et al. A SVM-based detection approach for GPS spoofing attacks to UAV[C]//Proceedings of the 2017 23rd International Conference on Automation and Computing, 2017: 1-11. [74] DASGUPTA S, GHOSH T, RAHMAN M. A reinforcement learning approach for global navigation satellite system spoofing attack detection in autonomous vehicles[J]. Transportation Research Record: Journal of the Transportation Research Board, 2022, 2676(12): 318-330. [75] DASGUPTA S, RAHMAN M, ISLAM M, et al. Prediction-based GNSS spoofing attack detection for autonomous vehicles[J]. arXiv: 2010.11722, 2020. [76] JIANG P, WU H, XIN C. DeepPOSE: detecting GPS spoofing attack via deep recurrent neural network[J]. Digital Communications and Networks, 2022, 8(5): 791-803. [77] DASGUPTA S, RAHMAN M, ISLAM M, et al. A sensor fusion-based GNSS spoofing attack detection framework for autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 23559-23572. [78] DANG Y, BENZAID C, SHEN Y, et al. GPS spoofing detector with adaptive trustable residence area for cellular based-UAVs[C]//Proceedings of the 2020 IEEE Global Communications Conference, 2020: 1-6. [79] DANG Y, BENZAID C, YANG B, et al. Deep learning for GPS spoofing detection in cellular-enabled UAV systems[C]//Proceedings of the 2021 International Conference on Networking and Network Applications, 2021: 501-506. [80] DANG Y, BENZAID C, YANG B, et al. Deep-ensemble-learning-based GPS spoofing detection for cellular-connected UAVs[J]. IEEE Internet of Things Journal, 2022, 9(24): 25068-25085. [81] DANG Y, KARAKOC A, NORSHAHIDA S, et al. 3D radio map-based GPS spoofing detection and mitigation for cellular-connected UAVs[J]. IEEE Transactions on Machine Learning in Communications and Networking, 2023, 1: 313-327. [82] DANG Y, KARAKOC A, JÄNTTI R. Graphic neural network based GPS spoofing detection for cellular-connected UAV swarm[C]//Proceedings of the 2023 IEEE 97th Vehicular Technology Conference, 2023: 1-6. |
[1] | TAO Linjuan, HUA Gengxing, LI Bo. Aspect-Level Sentiment Analysis Based on Location-Enhanced Word Embeddings and GRU-CNN Model [J]. Computer Engineering and Applications, 2024, 60(9): 212-218. |
[2] | LIAN Lu, TIAN Qichuan, TAN Run, ZHANG Xiaohang. Research Progress of Image Style Transfer Based on Neural Network [J]. Computer Engineering and Applications, 2024, 60(9): 30-47. |
[3] | ZHANG Junsan, XIAO Sen, GAO Hui, SHAO Mingwen, ZHANG Peiying, ZHU Jie. Multi-Task Graph Recommendation Algorithm Based on Neighborhood Sampling [J]. Computer Engineering and Applications, 2024, 60(9): 172-180. |
[4] | SONG Jianping, WANG Yi, SUN Kaiwei, LIU Qilie. Short Text Classification Combined with Hyperbolic Graph Attention Networks and Labels [J]. Computer Engineering and Applications, 2024, 60(9): 188-195. |
[5] | YANG Wentao, LEI Yuqi, LI Xingyue, ZHENG Tiancheng. Chinese Long Text Classification Model Based on BERT Fused Chinese Input Methods and BLCG [J]. Computer Engineering and Applications, 2024, 60(9): 196-202. |
[6] | DENG Xiquan, CHEN Gang. ConvUCaps: Medical Image Segmentation Model Based on Convolutional Capsule Network [J]. Computer Engineering and Applications, 2024, 60(8): 258-266. |
[7] | WANG Yonggui, WANG Xinru. Multi-View Group Recommendation Integrating Self-Attention and Graph Convolution [J]. Computer Engineering and Applications, 2024, 60(8): 287-295. |
[8] | QIAN Ping, HAN Rui, XIE Lingdong, LUO Wang, XU Huarong, LI Songsong, ZHENG Zhendong. Hardware Accelerator Supporting Inhibitory Spiking Neural Network [J]. Computer Engineering and Applications, 2024, 60(8): 338-347. |
[9] | PEI Wencan, SUN Guangwei, HUANG Jinguo, XU Dinghui, LIU Jing. Immediate Prediction Model of SPAD Value and Maturity of Fresh Tobacco Leaves in Field [J]. Computer Engineering and Applications, 2024, 60(8): 348-360. |
[10] | SUN Shilei, LI Ming, LIU Jing, MA Jingang, CHEN Tianzhen. Research Progress on Deep Learning in Field of Diabetic Retinopathy Classification [J]. Computer Engineering and Applications, 2024, 60(8): 16-30. |
[11] | WANG Weitai, WANG Xiaoqiang, LI Leixiao, TAO Yihao, LIN Hao. Review of Construction and Applications of Spatio-Temporal Graph Neural Network in Traffic Flow Prediction [J]. Computer Engineering and Applications, 2024, 60(8): 31-45. |
[12] | XIE Weiyu, ZHANG Qiang. Review on Detection of Drones and Birds in Photoelectric Images Based on Deep Learning Convolutional Neural Network [J]. Computer Engineering and Applications, 2024, 60(8): 46-55. |
[13] | SONG Shilin, ZHANG Xuejun. Algorithm Research Based on Multi-Feature Fusion of EEG Signals with Convolutional Neural Networks [J]. Computer Engineering and Applications, 2024, 60(8): 148-155. |
[14] | JIANG Liang, ZHANG Cheng, WEI Dejian, CAO Hui, DU Yuzheng. Deep Learning in Aided Diagnosis of Osteoporosis [J]. Computer Engineering and Applications, 2024, 60(7): 26-40. |
[15] | ZHENG Xiaoli, WANG Wei, DU Yuxuan, ZHANG Chuang. Demand Aware Attention Graph Neural Network for Session-Base Recommendation [J]. Computer Engineering and Applications, 2024, 60(7): 128-140. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||