![](/images/paper-icn.png)
A Comprehensive Approach to Combat GPS Spoofing and Ensure Security Positioning in Autonomous Vehicles
The emergence of Autonomous Vehicles (AVs) marks a significant turning point in the future of transportation and reflects radical advancements in artificial intelligence, edge computing, advanced sensing systems, and advanced control. These vehicles have sophisticated sensors, artificial intelligence systems, and computing capabilities that enable them to drive and operate without human intervention. They rely on various technologies such as cameras, radar, and Devices Satellite Locations )GPS( to obtain precise information about their surroundings and make real-time decisions. However, AVs face several unique challenges. They depend on accurate and reliable location information to drive and operate, ensuring safe driving and real-time decision-making. One of the major challenges is that central positioning systems are vulnerable to attacks, security breaches, spoofing attacks, and signal jamming, which can tamper with vehicle command systems. The significance of accurately determining the vehicle’s location lies in improving driving precision, efficient decision-making, enhanced mobility in different environments, and ensuring constant communication among vehicles for better collective performance. In this research, we propose an engineering model based on mixed collaboration for secure measurement in autonomous vehicle positioning. This collaboration enhances cooperation among vehicles, where the leader obtains its location through satellite identification, and the rest of the group members depend on the leader’s location to determine their positions in a highly reliable and immune manner against GPS spoofing, signal jamming, and fraudulent attacks. Additionally, to ensure secure communication among different vehicles, a strong encryption system has been adopted to send messages within the proposed framework, ensuring higher reliability. The technique used is lightweight and robust because it uses only one operation of multiplication and only one exponential operation. Moreover, network traffic analysis and the complexity of different algorithms have been assessed to ensure the efficiency and effectiveness of the proposed framework.
[1] Abrar M., Islam R., Satam S., Shao S., and Hariri, S., “GPS-IDS: An Anomaly-based GPS Spoofing Attack Detection Framework for Autonomous Vehicles,” arXiv Preprint, vol. arXiv:2405.08359, 2024. https://doi.org/10.48550/arXiv.2405.08359
[2] Alheeti K., Alzahrani A., and Al-Dosary D., “LiDAR Spoofing Attack Detection in Autonomous Vehicles,” in Proceedings of the IEEE International Conference on Consumer Electronics, Las Vegas, pp. 1-2, 2022. Doi:10.1109/ICCE53296.2022.9730540
[3] Almeaibed S., Al-Rubaye S., Tsourdos A., and Avdelidis N., “Digital Twin Analysis to Promote Safety and Security in Autonomous Vehicles,” IEEE Communications Standards Magazine, vol. 5, no. 1, pp. 40-46, 2021. DOI:10.1109/MCOMSTD.011.2100004
[4] Belin A., Myers R., Ruan S., S´arosi G., and Speranza A., “Complexity Equals Anything II,” Journal of High Energy Physics, vol. 1, no. 154, pp. 1-79, 2023. https://doi.org/10.1007/JHEP01(2023)154
[5] Chen L., Wu P., Chitta K., Jaeger B., and Geiger A., “End-to-End Autonomous Driving: Challenges and Frontiers,” arXiv Preprint, vol. arXiv:2306.16927, 2023. https://doi.org/10.48550/arXiv.2306.16927
[6] Chen Q., Liu P., Li G., and Wang Z., “GPS Attack Detection and Mitigation for Safe Autonomous Driving Using Image and Map-based Lateral Direction Localization,” arXiv Preprint, vol. arXiv:2310.05407, 2023. 634 The International Arab Journal of Information Technology, Vol. 21, No. 4, July 2024 https://doi.org/10.48550/arXiv.2310.05407
[7] Cui J., Liew L., Sabaliauskaite G., and Zhou F., “A Review on Safety Failures, Security Attacks, and Available Countermeasures for Autonomous Vehicles,” Ad Hoc Networks, vol. 90, pp. 101823, 2019. https://doi.org/10.1016/j.adhoc.2018.12.006
[8] Cui J., Sabaliauskaite G., Liew L., Zhou F., and Zhang B., “Collaborative Analysis Framework of Safety and Security for Autonomous Vehicles,” IEEE Access, vol. 7, pp. 148672-148683, 2019. DOI:10.1109/ACCESS.2019.2946632
[9] Dasgupta S., Ahmed A., Rahman M., and Bandi T., “Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience,” arXiv Preprint, arXiv:2401.01394, 2024. https://doi.org/10.48550/arXiv.2401.01394
[10] Dasgupta S., Ghosh T., and Rahman M. “A Reinforcement Learning Approach for Global Navigation Satellite System Spoofing Attack Detection in Autonomous Vehicles,” Journal of the Transportation Research Board, vol. 2676, no. 12, pp. 318-330, 2022. https://doi.org/10.1177/03611981221095509
[11] Dasgupta S., Rahman M., Islam M., and Chowdhury M., “Prediction-based GNSS Spoofing Attack Detection for Autonomous Vehicles,” arXiv Preprint, vol. arXiv:2010.11722, 2020. https://doi.org/10.48550/arXiv.2010.11722
[12] Dutta R., Yu F., Zhang T., Hu Y., and Jin Y., “Security for Safety: A Path Toward Building Trusted Autonomous Vehicles,” in Proceedings of the IEEE/ACM International Conference on Computer-Aided Design, San Diego, pp. 1-6, 2018. DOI:10.1145/3240765.3243496
[13] Gu Z., Wang Z., Liu Z., and Saberi M., “Network Traffic Instability with Automated Driving and Cooperative Merging,” Transportation Research Part C: Emerging Technologies, vol. 138, pp. 103626, 2022. https://doi.org/10.1016/j.trc.2022.103626
[14] Habib A., Laouid A., and Kara M., “Secure Consensus Clock Synchronization in Wireless Sensor Networks,” in Proceedings of the International Conference on Artificial Intelligence for Cyber Security Systems and Privacy, El Oued, pp. 1-6, 2021. DOI:10.1109/AI- CSP52968.2021.9671225
[15] Han H., Yan Z., Jing X., and Pedrycz W., “Applications of Sketches in Network Traffic Measurement: A Survey,” Information Fusion, vol. 82, pp. 58-85, 2022. https://doi.org/10.1016/j.inffus.2021.12.007
[16] Hazmy I., Hawbani A., Wang X., Al-Dubai A., and Ghannami A., “Potential of Satellite-Airborne Sensing Technologies for Agriculture 4.0 and Climate-Resilient: A Review,” IEEE Sensors Journal, vol. 24, no. 4, pp. 4161-4180, 2023. DOI:10.1109/JSEN.2023.3343428
[17] Hidalgo C., “Economic Complexity Theory and Applications,” Nature Reviews Physics, vol. 3, no. 2, pp. 92-113, 2021. https://doi.org/10.1038/s42254-020-00275-1
[18] Kara M., Karampidis K., Papadourakis G., Laouid A., and AlShaikh M., “A Probabilistic Public-Key Encryption with Ensuring Data Integrity in Cloud Computing,” in Proceedings of the International Conference on Control, Artificial Intelligence, Robotics and Optimization, Crete, pp. 59-66, 2023. DOI:10.1109/ICCAIRO58903.2023.00017
[19] Kara M., Karampidis K., Sayah Z., Laouid A., and Papadourakis G., “A Password-Based Mutual Authentication Protocol via Zero-Knowledge Proof Solution,” in Proceedings of the International Conference on Applied CyberSecurity, Dubai, pp. 31-40, 2023.
[20] Kara M., Laouid A., and Hammoudeh M., “An Efficient Multi-Signature Scheme for Blockchain,” Cryptology ePrint Archive, 2023. https://eprint.iacr.org/2023/078
[21] Kara M., Laouid A., Hammoudeh M., and Bounceur A., “One-Digit Checksum for Data Integrity Verification of Cloud-Executed Homomorphic Encryption Operations,” Cryptology ePrint Archive, 2023.
[22] Komissarov R. and Wool A., “Spoofing Attacks Against Vehicular FMCW Radar,” in Proceedings of the 5th Workshop on Attacks and Solutions in Hardware Security, New York, pp. 91-97, 2021. https://doi.org/10.1145/3474376.3487283
[23] Lee S., “Opinions of Active Transportation Users on Policies to Ensure their perceived safety in the Era of Autonomous Vehicles,” Case Studies on Transport Policy, vol. 12, pp. 101002, 2023. https://doi.org/10.1016/j.cstp.2023.101002
[24] Nanda A., Puthal D., Rodrigues J., and Kozlov S., “Internet of Autonomous Vehicles Communications Security: Overview, Issues, and Directions,” IEEE Wireless Communications, vol. 26, no. 4, pp. 60-65, 2019. DOI:10.1109/MWC.2019.1800503
[25] Papadogiannaki E. and Ioannidis S., “A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures,” ACM Computing Surveys, vol. 54, no. 6, pp. 1-35, 2021. https://doi.org/10.1145/3457904
[26] Shabbir M., Kamal M., Ullah Z. and Khan M., “Securing Autonomous Vehicles Against GPS Spoofing Attacks: A Deep Learning Approach,” IEEE Access, vol. 11, pp. 105513-105526, 2023. DOI:10.1109/ACCESS.2023.3319514
[27] Xiong S., Li B., Zhu S., Cui D., and Song X., “Spatial Pyramid Pooling and Adaptively Feature Fusion based Yolov3 for Traffic Sign Detection,” The International Arab Journal of Information A Comprehensive Approach to Combat GPS Spoofing and Ensure Security Positioning in ... 635 Technology, vol. 20, no. 4, pp. 592-599, 2023. DOI:10.34028/iajit/20/4/5
[28] Wiseman Y., Autonomous Vehicles, IGI Global, 2022. DOI:10.4018/978-1-6684-3694-3.ch043
[29] Xu W., Yan C., Jia W., Ji X., and Liu J., “Analyzing and Enhancing the Security of Ultrasonic Sensors for Autonomous Vehicles,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 5015-5029, 2018. DOI:10.1109/JIOT.2018.2867917