Survey of Detection Techniques for GPS Spoofing in Connected Vehicles: Taxonomy, Evaluation, and Future Research Directions
Abstract
The Global Positioning System (GPS) plays a pivotal role in modern vehicular systems by providing accurate navigation and timing services. However, its unencrypted and weak signal structure makes it vulnerable to spoofing attacks, especially in connected vehicle environments. This survey comprehensively reviews GPS spoofing detection techniques, categorizing them into five main approaches: signal processing, machine learning (ML), anomaly detection, cryptographic techniques, and sensor fusion. For each category, we analyze the methods’ underlying principles, datasets used (e.g., GNSS-SDR, USRP, simulation-based testbeds), and performance metrics including accuracy, precision, recall, and F1 score. The comparative evaluation highlights that hybrid methods and sensor fusion approaches generally offer higher robustness, while ML-based methods achieve high accuracy but require extensive training data. The paper also identifies key challenges, such as a lack of benchmarking standards and performance variability across dynamic environments. Future directions are proposed to address these limitations and improve the reliability and scalability of GPS spoofing detection systems in real-world connected vehicle applications.
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F. Z. Bousbaa, C. A. Kerrache, N. Lagraa, R. Hussain, M. B. Yagoubi, and A. E. K. Tahari, “Group data communication in connected vehicles: A survey,” Vehicular Communications, vol. 37, p. 100518, 2022. Elsevier. https://doi.org/10.1016/j.vehcom.2022.100518.
R. Singh et al., “Analysis of network slicing for management of 5G networks using machine learning techniques,” Wirel Commun Mob Comput, vol. 2022, no. 1, p. 9169568, 2022. Wiley Online Library. https://doi.org/10.1155/2022/9169568.
B. Pourghebleh and N. J. Navimipour, “Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research,” Journal of Network and Computer Applications, vol. 97, pp. 23–34, 2017. Elsevier. https://doi.org/10.1016/j.jnca.2017.08.006.
B. Pourghebleh and N. Jafari Navimipour, “Towards efficient data collection mechanisms in the vehicular ad hoc networks,” International Journal of Communication Systems, vol. 32, no. 5, p. e3893, 2019. Wiley Online Library. https://doi.org/10.1002/dac.3893.
A. Mehbodniya, J. L. Webber, and S. Karupusamy, “Improving the geo-drone-based route for effective communication and connection stability improvement in the emergency area ad-hoc network,” Sustainable Energy Technologies and Assessments, vol. 53, p. 102558, 2022. Elsevier. https://doi.org/10.1016/j.seta.2022.102558.
B. Pourghebleh, N. Hekmati, Z. Davoudnia, and M. Sadeghi, “A roadmap towards energy‐efficient data fusion methods in the Internet of Things,” Concurr Comput, vol. 34, no. 15, p. e6959, 2022. Wiley Online Library. https://doi.org/10.1002/cpe.6959.
M. S. Gharajeh and H. B. Jond, “Hybrid global positioning system-adaptive neuro-fuzzy inference system based autonomous mobile robot navigation,” Rob Auton Syst, vol. 134, p. 103669, 2020. Elsevier. https://doi.org/10.1016/j.robot.2020.103669.
G. Oligeri, S. Sciancalepore, O. A. Ibrahim, and R. Di Pietro, “GPS spoofing detection via crowd-sourced information for connected vehicles,” Computer Networks, vol. 216, p. 109230, 2022. Elsevier. https://doi.org/10.1016/j.comnet.2022.109230.
S. Hakak et al., “Autonomous Vehicles in 5G and beyond: A Survey,” Vehicular Communications, vol. 39, p. 100551, 2023. Elsevier. https://doi.org/10.1016/j.vehcom.2022.100551.
A. Nanda, D. Puthal, J. J. P. C. Rodrigues, and S. A. Kozlov, “Internet of autonomous vehicles communications security: overview, issues, and directions,” IEEE Wirel Commun, vol. 26, no. 4, pp. 60–65, 2019. IEEE. https://doi.org/10.1109/MWC.2019.1800503.
S. Dasgupta, T. Ghosh, and M. Rahman, “A reinforcement learning approach for global navigation satellite system spoofing attack detection in autonomous vehicles,” Transp Res Rec, vol. 2676, no. 12, pp. 318–330, 2022. Sage Publications. https://doi.org/10.1177/03611981221095509.
J. Cui, L. S. Liew, G. Sabaliauskaite, and F. Zhou, “A review on safety failures, security attacks, and available countermeasures for autonomous vehicles,” Ad Hoc Networks, vol. 90, p. 101823, 2019. Elsevier. https://doi.org/10.1016/j.adhoc.2018.12.006.
A. Peivandizadeh and B. Molavi, “Compatible Authentication and Key Agreement Protocol for Low Power and Lossy Network in Iot Environment.,” Available at SSRN 4454407, 2023. Research Square. https://doi.org/10.21203/rs.3.rs-2085426/v1.
B. Pourghebleh and V. Hayyolalam, “A comprehensive and systematic review of the load balancing mechanisms in the Internet of Things,” Cluster Comput, vol. 23, no. 2, pp. 641–661, 2020. Springer. https://doi.org/10.1007/s10586-019-02950-0.
S. Aghakhani, A. Larijani, F. Sadeghi, D. Martín, and A. A. Shahrakht, “A Novel hybrid artificial bee colony-based deep convolutional neural network to improve the detection performance of backscatter communication systems,” Electronics (Basel), vol. 12, no. 10, p. 2263, 2023. MDPI. https://doi.org/10.3390/electronics12102263.
J. Webber, A. Mehbodniya, Y. Hou, K. Yano, and T. Kumagai, “Study on idle slot availability prediction for WLAN using a probabilistic neural network,” in 2017 23rd Asia-Pacific Conference on Communications (APCC), IEEE, Perth, WA, Australia, 2017, pp. 1–6. https://doi.org/10.23919/APCC.2017.8304030.
M. Bagheri et al., “Data conditioning and forecasting methodology using machine learning on production data for a well pad,” in Offshore technology conference, OTC, 2020, p. D031S037R002. One Petro. https://doi.org/10.4043/30854-MS.
A. Niazi, R. Amrollahi, and H. Sadeghi, “Design of a high-efficiency dual-helical antenna for microwave plasma sources,” IEEE Transactions on Plasma Science, vol. 50, no. 2, pp. 203–209, 2022. IEEE. https://doi.org/10.1109/TPS.2022.3144201.
S. Saeidi, S. Nazari Enjedani, E. Alvandi Behineh, K. Tehranian, and S. Jazayerifar, “Factors affecting public transportation use during pandemic: an integrated approach of technology acceptance model and theory of planned behavior,” Tehnički glasnik, vol. 18, no. 3, pp. 342–353, 2024. Srce. https://doi.org/10.31803/tg-20230601145322.
M. Pham and K. Xiong, “A survey on security attacks and defense techniques for connected and autonomous vehicles,” Comput Secur, vol. 109, p. 102269, 2021. Elsevier. https://doi.org/10.1016/j.cose.2021.102269.
Z. Yang et al., “Anomaly detection against GPS spoofing attacks on connected and autonomous vehicles using learning from demonstration,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9462–9475, 2023. IEEE. https://doi.org/10.1109/TITS.2023.3269029.
B. R. Mudhivarthi, P. Thakur, and G. Singh, “Aspects of cyber security in autonomous and connected vehicles,” Applied Sciences, vol. 13, no. 5, p. 3014, 2023. MDPI. https://doi.org/10.3390/app13053014.
B. M. Jafari, M. Zhao, and A. Jafari, “Rumi: an intelligent agent enhancing learning management systems using machine learning techniques,” Journal of Software Engineering and Applications, vol. 15, no. 9, pp. 325–343, 2022. Scientific Research. https://doi.org/10.4236/jsea.2022.159019.
Z. Li, C. Han, and D. W. Coit, “System reliability models with dependent degradation processes,” in Advances in reliability and maintainability methods and engineering applications: essays in honor of professor Hong-Zhong Huang on his 60th birthday, Springer, 2023, pp. 475–497. https://doi.org/10.1007/978-3-031-28859-3_19.
S. N. H. Bukhari, J. Webber, and A. Mehbodniya, “Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates,” Sci Rep, vol. 12, no. 1, p. 7810, 2022. Nature. https://doi.org/10.1038/s41598-022-11731-6.
K. Tehranian, “Can machine learning catch economic recessions using economic and market sentiments?,” arXiv preprint arXiv:2308.16200, 2023. Cornell University. https://doi.org/10.48550/arXiv.2308.16200.
C. Han and X. Fu, “Challenge and opportunity: deep learning-based stock price prediction by using Bi-directional LSTM model,” Frontiers in Business, Economics and Management, vol. 8, no. 2, pp. 51–54, 2023.
T. Gera, J. Singh, A. Mehbodniya, J. L. Webber, M. Shabaz, and D. Thakur, “Dominant feature selection and machine learning‐based hybrid approach to analyze android ransomware,” Security and Communication Networks, vol. 2021, no. 1, p. 7035233, 2021. Wiley Online Library. https://doi.org/10.1155/2021/7035233.
M. Khorshidi, M. Ameri, and A. Goli, “Cracking performance evaluation and modelling of RAP mixtures containing different recycled materials using deep neural network model,” Road Materials and Pavement Design, vol. 25, no. 4, pp. 716–735, 2024. Taylor & Francis. https://doi.org/10.1080/14680629.2023.2222835.
M. Khorshidi, A. Goli, M. Orešković, K. Khayambashi, and M. Ameri, “Performance evaluation of asphalt mixtures containing different proportions of alternative materials,” Sustainability, vol. 15, no. 18, p. 13314, 2023. MDPI. https://doi.org/10.3390/su151813314.
S. Hussein, A. Krings, and A. Azadmanesh, “VANET clock synchronization for resilient DSRC safety applications,” in 2017 Resilience Week (RWS), IEEE, Wilmington, DE, USA, 2017, pp. 57–63. https://doi.org/10.1109/RWEEK.2017.8088648.
M. L. Psiaki, B. W. O’Hanlon, J. A. Bhatti, D. P. Shepard, and T. E. Humphreys, “GPS spoofing detection via dual-receiver correlation of military signals,” IEEE Trans Aerosp Electron Syst, vol. 49, no. 4, pp. 2250–2267, 2013. IEEE. https://doi.org/10.1109/TAES.2013.6621814.
B. W. O’Hanlon, M. L. Psiaki, J. A. Bhatti, D. P. Shepard, and T. E. Humphreys, “Real‐time GPS spoofing detection via correlation of encrypted signals,” Navigation, vol. 60, no. 4, pp. 267–278, 2013. Wiley Online Library. https://doi.org/10.1002/navi.44.
B. W. O’Hanlon, M. L. Psiaki, J. A. Bhatti, D. P. Shepard, and T. E. Humphreys, “Real‐time GPS spoofing detection via correlation of encrypted signals,” Navigation, vol. 60, no. 4, pp. 267–278, 2013. Wiley Online Library. https://doi.org/10.1002/navi.44.
S. Bhamidipati, T. Y. Mina, and G. X. Gao, “GPS time authentication against spoofing via a network of receivers for power systems,” in 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), IEEE, Monterey, CA, USA, 2018, pp. 1485–1491. https://doi.org/10.1109/PLANS.2018.8373542.
B. W. O’Hanlon, M. L. Psiaki, T. E. Humphreys, and J. A. Bhatti, “Real-time spoofing detection using correlation between two civil GPS receiver,” in Proceedings of the 25th international technical meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2012), 2012, pp. 3584–3590. ION.
M. L. Psiaki, B. W. O’Hanlon, J. A. Bhatti, D. P. Shepard, and T. E. Humphreys, “Civilian GPS spoofing detection based on dual-receiver correlation of military signals,” in Proceedings of the 24th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2011), 2011, pp. 2619–2645. ION.
Y. Hu, X. Dong, Z. Wu, and Z. Shi, “Spoofing mitigation for GPS receiver based on array antenna using cross-correlation of received signals of each element,” in 2020 Chinese Automation Congress (CAC), IEEE, Shanghai, China, 2020, pp. 7295–7300. https://doi.org/10.1109/CAC51589.2020.9326636.
Y. Dang, C. Benzaïd, Y. Shen, and T. Taleb, “GPS spoofing detector with adaptive trustable residence area for cellular based-UAVs,” in GLOBECOM 2020-2020 IEEE Global Communications Conference, IEEE, Taipei, Taiwan, 2020, pp. 1–6. https://doi.org/10.1109/GLOBECOM42002.2020.9348030.
T. Yasmin Mina, S. Bhamidipati, and G. Xingxin Gao, “GPS spoofing detection for the power grid network using a multireceiver hierarchical framework architecture,” Navigation, vol. 66, no. 4, pp. 857–875, 2019. Wiley Online Library. https://doi.org/10.1002/navi.341.
E. Schmidt, N. Gatsis, and D. Akopian, “A GPS spoofing detection and classification correlator-based technique using the LASSO,” IEEE Trans Aerosp Electron Syst, vol. 56, no. 6, pp. 4224–4237, 2020. IEEE. https://doi.org/10.1109/TAES.2020.2990149.
C. Shao, Z. Miao, B. Chen, Y. Cui, H. Li, and H. Shu, “An Attack Detection Method Based on Spatiotemporal Correlation for Autonomous Vehicles Sensors,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, Macau, China, 2022, pp. 2187–2193. https://doi.org/10.1109/ITSC55140.2022.9921882.
L. He, W. Li, C. Guo, and R. Niu, “Civilian unmanned aerial vehicle vulnerability to gps spoofing attacks,” in 2014 Seventh International Symposium on Computational Intelligence and Design, IEEE, Hangzhou, China, 2014, pp. 212–215. https://doi.org/10.1109/ISCID.2014.131.
K. D. Wesson, J. N. Gross, T. E. Humphreys, and B. L. Evans, “GNSS signal authentication via power and distortion monitoring,” IEEE Trans Aerosp Electron Syst, vol. 54, no. 2, pp. 739–754, 2017. IEEE. https://doi.org/10.1109/TAES.2017.2765258.
A. Singandhupe, H. M. La, and D. Feil-Seifer, “Reliable security algorithm for drones using individual characteristics from an EEG signal,” IEEE Access, vol. 6, pp. 22976–22986, 2018. IEEE. https://doi.org/10.1109/ACCESS.2018.2827362.
J. Magiera and R. Katulski, “Detection and mitigation of GPS spoofing based on antenna array processing,” Journal of applied research and technology, vol. 13, no. 1, pp. 45–57, 2015. SCIELO.
W. Feng, J.-M. Friedt, G. Goavec-Merou, and F. Meyer, “Software-defined radio implemented GPS spoofing and its computationally efficient detection and suppression,” IEEE Aerospace and Electronic Systems Magazine, vol. 36, no. 3, pp. 36–52, 2021. IEEE. https://doi.org/10.1109/MAES.2020.3040491.
D. S. Radin, GPS spoofing detection using multiple antennas and individual space vehicle pseudoranges. University of Rhode Island, 2015.
T. Talaei Khoei, S. Ismail, K. Al Shamaileh, V. K. Devabhaktuni, and N. Kaabouch, “Impact of dataset and model parameters on machine learning performance for the detection of GPS spoofing attacks on unmanned aerial vehicles,” Applied Sciences, vol. 13, no. 1, p. 383, 2022. MDPI. https://doi.org/10.3390/app13010383.
T. Talaei Khoei, S. Ismail, and N. Kaabouch, “Dynamic selection techniques for detecting GPS spoofing attacks on UAVs,” Sensors, vol. 22, no. 2, p. 662, 2022. MDPI. https://doi.org/10.3390/s22020662.
J. Jetto, R. Gandhiraj, G. A. Shanmugha Sundaram, and K. P. Soman, “Software Defined Radio-Based GPS Spoofing Attack Model on Road Navigation System,” in Soft Computing and Signal Processing: Proceedings of 3rd ICSCSP 2020, Volume 2, Springer, Singapore, 2022, pp. 339–350. https://doi.org/10.1007/978-981-16-1249-7_32.
X. Zhu, T. Hua, F. Yang, G. Tu, and X. Chen, “Global positioning system spoofing detection based on support vector machines,” IET Radar, Sonar & Navigation, vol. 16, no. 2, pp. 224–237, 2022. Wiley Online Library. https://doi.org/10.1049/rsn2.12178.
G. Panice et al., “A SVM-based detection approach for GPS spoofing attacks to UAV,” in 2017 23rd International Conference on Automation and Computing (ICAC), IEEE, Huddersfield, UK, 2017, pp. 1–11. https://doi.org/10.23919/IConAC.2017.8081999.
S. Semanjski, I. Semanjski, W. De Wilde, and A. Muls, “Cyber-threats analytics for detection of GNSS spoofing,” in 7th International Conference on Data Analytics, IARIA, 2018, pp. 136–140.
S. Semanjski, I. Semanjski, W. De Wilde, and A. Muls, “Use of supervised machine learning for GNSS signal spoofing detection with validation on real-world meaconing and spoofing data—Part I,” Sensors, vol. 20, no. 4, p. 1171, 2020. MDPI. https://doi.org/10.3390/s20041171.
G. Aissou, H. O. Slimane, S. Benouadah, and N. Kaabouch, “Tree-based supervised machine learning models for detecting GPS spoofing attacks on UAS,” in 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE, New York, NY, USA, 2021, pp. 649–653. https://doi.org/10.1109/UEMCON53757.2021.9666744.
B. Sousa, N. Magaia, and S. Silva, “An intelligent intrusion detection system for 5g-enabled internet of vehicles,” Electronics (Basel), vol. 12, no. 8, p. 1757, 2023. MDPI. https://doi.org/10.3390/electronics12081757.
A. Khadka, P. Karypidis, A. Lytos, and G. Efstathopoulos, “A benchmarking framework for cyber-attacks on autonomous vehicles,” Transportation research procedia, vol. 52, pp. 323–330, 2021. Elsevier. https://doi.org/10.1016/j.trpro.2021.01.038.
M. R. Norouzian, P. Xu, C. Eckert, and A. Zarras, “Hybroid: Toward android malware detection and categorization with program code and network traffic,” in Information Security: 24th International Conference, ISC 2021, Virtual Event, November 10–12, 2021, Proceedings 24, Springer, Cham, 2021, pp. 259–278. https://doi.org/10.1007/978-3-030-91356-4_14.
M. R. Manesh, J. Kenney, W. C. Hu, V. K. Devabhaktuni, and N. Kaabouch, “Detection of GPS spoofing attacks on unmanned aerial systems,” in 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), IEEE, Las Vegas, NV, USA, 2019, pp. 1–6. https://doi.org/10.1109/CCNC.2019.8651804.
M. Sun, Y. Qin, J. Bao, and X. Yu, “GPS Spoofing Detection Based on Decision Fusion with a K-out-of-N Rule.,” Int. J. Netw. Secur., vol. 19, no. 5, pp. 670–674, 2017.
K. Xiao, J. Zhao, Y. He, C. Li, and W. Cheng, “Abnormal behavior detection scheme of UAV using recurrent neural networks,” IEEE Access, vol. 7, pp. 110293–110305, 2019. IEEE. https://doi.org/10.1109/ACCESS.2019.2934188.
S. C. Bose, “GPS spoofing detection by neural network machine learning,” IEEE Aerospace and Electronic Systems Magazine, vol. 37, no. 6, pp. 18–31, 2021. IEEE. https://doi.org/10.1109/MAES.2021.3100844.
E. Shafiee, M. R. Mosavi, and M. Moazedi, “Detection of spoofing attack using machine learning based on multi-layer neural network in single-frequency GPS receivers,” The Journal of Navigation, vol. 71, no. 1, pp. 169–188, 2018. Cambridge University Press. https://doi.org/10.1017/S0373463317000558.
P. Jiang, H. Wu, and C. Xin, “DeepPOSE: Detecting GPS spoofing attack via deep recurrent neural network,” Digital Communications and Networks, vol. 8, no. 5, pp. 791–803, 2022. Elsevier. https://doi.org/10.1016/j.dcan.2021.09.006.
Y.-H. Sung, S.-J. Park, D.-Y. Kim, and S. Kim, “GPS spoofing detection method for small UAVs using 1D convolution neural network,” Sensors, vol. 22, no. 23, p. 9412, 2022. MDPI. https://doi.org/10.3390/s22239412.
J. Campos et al., “A machine learning based smartphone app for gps spoofing detection,” in Security and Privacy in Communication Networks: 16th EAI International Conference, SecureComm 2020, Washington, DC, USA, October 21-23, 2020, Proceedings, Part II 16, Springer, Cham, 2020, pp. 235–241. https://doi.org/10.1007/978-3-030-63095-9_13.
M. Strohmeier, V. Lenders, and I. Martinovic, “Lightweight location verification in air traffic surveillance networks,” in Proceedings of the 1st ACM Workshop on Cyber-Physical System Security, 2015, pp. 49–60. ACM Digital Library. https://doi.org/10.1145/2732198.2732202.
Z. Feng, C. K. Seow, and Q. Cao, “GNSS Anti-spoofing Detection based on Gaussian Mixture Model Machine Learning,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, Macau, China, 2022, pp. 3334–3339. https://doi.org/10.1109/ITSC55140.2022.9922109.
J. Hardy et al., “Unmanned aerial vehicle relative navigation in GPS denied environments,” in 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS), IEEE, Savannah, GA, USA, 2016, pp. 344–352. https://doi.org/10.1109/PLANS.2016.7479719.
X. Han et al., “ADS-lead: Lifelong anomaly detection in autonomous driving systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 1039–1051, 2022. IEEE. https://doi.org/10.1109/TITS.2021.3122906.
F. Van Wyk, Y. Wang, A. Khojandi, and N. Masoud, “Real-time sensor anomaly detection and identification in automated vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1264–1276, 2019. IEEE. https://doi.org/10.1109/TITS.2019.2906038.
J. Whelan, T. Sangarapillai, O. Minawi, A. Almehmadi, and K. El-Khatib, “Novelty-based intrusion detection of sensor attacks on unmanned aerial vehicles,” in Proceedings of the 16th ACM symposium on QoS and security for wireless and mobile networks, 2020, pp. 23–28. https://doi.org/10.1145/3416013.3426446.
J. Whelan, A. Almehmadi, and K. El-Khatib, “Artificial intelligence for intrusion detection systems in unmanned aerial vehicles,” Computers and Electrical Engineering, vol. 99, p. 107784, 2022. Elsevier. https://doi.org/10.1016/j.compeleceng.2022.107784.
A. Xue, F. Xu, J. Xu, J. H. Chow, S. Leng, and T. Bi, “Online pattern recognition and data correction of PMU data under GPS spoofing attack,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 6, pp. 1240–1249, 2020. SGEPRI. https://doi.org/10.35833/MPCE.2019.000365.
X. Han et al., “A unified anomaly detection methodology for lane-following of autonomous driving systems,” in 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), IEEE, New York City, NY, USA, 2021, pp. 836–844. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00119.
E. Basan, A. Basan, A. Nekrasov, C. Fidge, N. Sushkin, and O. Peskova, “GPS-spoofing attack detection technology for UAVs based on Kullback–Leibler divergence,” Drones, vol. 6, no. 1, p. 8, 2021. MDPI. https://doi.org/10.3390/drones6010008.
T. Behdadnia and G. Deconinck, “Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning,” arXiv preprint arXiv:2209.08099, 2022. Cornell University. https://doi.org/10.48550/arXiv.2209.08099.
D. Sharma, S. K. Gupta, A. Rashid, S. Gupta, M. Rashid, and A. Srivastava, “A novel approach for securing data against intrusion attacks in unmanned aerial vehicles integrated heterogeneous network using functional encryption technique,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 7, p. e4114, 2021. Wiley Online Library. https://doi.org/10.1002/ett.4114.
M. Babaghayou, N. Labraoui, A. A. A. Ari, N. Lagraa, M. A. Ferrag, and L. Maglaras, “SAMA: Security-aware monitoring approach for location abusing and UAV GPS-spoofing attacks on Internet of Vehicles,” in International Conference on Cognitive Radio Oriented Wireless Networks, Springer, Cham, 2021, pp. 343–360. https://doi.org/10.1007/978-3-030-98002-3_25.
L. Chaari, S. Chahbani, and J. Rezgui, “Vulnerabilities assessment for unmanned aerial vehicles communication systems,” in 2020 International Symposium on Networks, Computers and Communications (ISNCC), IEEE, Montreal, QC, Canada, 2020, pp. 1–6. https://doi.org/10.1109/ISNCC49221.2020.9297293.
M. Umar, J. Wang, L. Liu, Z. Guo, and S. Wang, “Physical layer authentication in the internet of vehicles based on signal propagation attribute prediction,” Journal of Networking and Network Applications, vol. 3, no. 1, pp. 1–10, 2023. IEC science. https://iecscience.org/jpapers/129.
K. N. Qureshi, M. A. S. Sandila, I. T. Javed, T. Margaria, and L. Aslam, “Authentication scheme for unmanned aerial vehicles based internet of vehicles networks,” Egyptian Informatics Journal, vol. 23, no. 1, pp. 83–93, 2022. Elsevier. https://doi.org/10.1016/j.eij.2021.07.001.
D. Yang, Y. Zhao, Z. Yi, D. Yang, and S. He, “A Design Scheme of Data Security for Unmanned Aerial Vehicles,” in International Conference on Advanced Hybrid Information Processing, Springer, Cham, 2022, pp. 165–178. https://doi.org/10.1007/978-3-031-28787-9_13.
A. Jain, J. Singh, S. Kumar, Țurcanu Florin-Emilian, M. Traian Candin, and P. Chithaluru, “Improved recurrent neural network schema for validating digital signatures in VANET,” Mathematics, vol. 10, no. 20, p. 3895, 2022. MDPI. https://doi.org/10.3390/math10203895.
S. Wang, J. Wang, C. Su, and X. Ma, “Intelligent detection algorithm against UAVs’ GPS spoofing attack,” in 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), IEEE, Hong Kong, 2020, pp. 382–389. https://doi.org/10.1109/ICPADS51040.2020.00058.
S. Dasgupta, M. Rahman, M. Islam, and M. Chowdhury, “A sensor fusion-based GNSS spoofing attack detection framework for autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 23559–23572, 2022. IEEE. https://doi.org/10.1109/TITS.2022.3197817.
J. García, J. M. Molina, and J. Trincado, “Real evaluation for designing sensor fusion in UAV platforms,” Information Fusion, vol. 63, pp. 136–152, 2020. Elsevier. https://doi.org/10.1016/j.inffus.2020.06.003.
M. Kamal, A. Barua, C. Vitale, C. Laoudias, and G. Ellinas, “GPS location spoofing attack detection for enhancing the security of autonomous vehicles,” in 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), IEEE, Norman, OK, USA, 2021, pp. 1–7. https://doi.org/10.1109/VTC2021-Fall52928.2021.9625567.
L. Meng, S. Ren, G. Tang, C. Yang, and W. Yang, “Uav sensor spoofing detection algorithm based on gps and optical flow fusion,” in Proceedings of the 2020 4th International Conference on Cryptography, Security and Privacy, 2020, pp. 146–151. https://doi.org/10.1145/3377644.3377670.
S. E. Meheretu, S. Y. Hailesilassie, and E. Nigussie, “GNSS-Independent Navigation of UAV through Utilization of Sensor Fusion and Intelligence System,” in 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA), IEEE, Bahir Dar, Ethiopia, 2022, pp. 181–186. https://doi.org/10.1109/ICT4DA56482.2022.9971437.
M. Panoff, R. G. Dutta, Y. Hu, K. Yang, and Y. Jin, “On sensor security in the era of IoT and CPS,” SN Comput Sci, vol. 2, no. 1, p. 51, 2021. Springer. https://doi.org/10.1007/s42979-020-00423-5.
S. M. Albrektsen, T. H. Bryne, and T. A. Johansen, “Robust and secure UAV navigation using GNSS, phased-array radio system and inertial sensor fusion,” in 2018 IEEE Conference on Control Technology and Applications (CCTA), IEEE, Copenhagen, Denmark, 2018, pp. 1338–1345. https://doi.org/10.1109/CCTA.2018.8511354.
DOI: https://doi.org/10.31449/inf.v49i27.9504
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