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Improving Unmanned Aerial Vehicle Security as a Factor in Sustainable Development of Smart City Infrastructure: Automatic Dependent Surveillance–Broadcast (ADS-B) Data Protection

Author

Listed:
  • Serhii Semenov

    (Institute of Security and Computer Science, University of the National Education Commission, ul. Podchorążych 2, 30-084 Krakow, Poland
    Faculty of Cyber Defense, SET University, 01033 Kyiv, Ukraine)

  • Magdalena Krupska-Klimczak

    (Institute of Security and Computer Science, University of the National Education Commission, ul. Podchorążych 2, 30-084 Krakow, Poland)

  • Patryk Mazurek

    (Institute of Security and Computer Science, University of the National Education Commission, ul. Podchorążych 2, 30-084 Krakow, Poland)

  • Minjian Zhang

    (Zhejiang Nova Intelligent Technology Co., Ltd., Huzhou 318025, China)

  • Olena Chernikh

    (Department of “Computer Engineering and Programming”, National Technical University “Kharkiv Polytechnic Institute”, 61000 Kharkiv, Ukraine)

Abstract

The rapid integration of Unmanned Aerial Vehicles (UAVs) into smart city infrastructures necessitates advanced security measures to ensure their safe and sustainable operation. However, existing Automatic Dependent Surveillance–Broadcast (ADS-B) systems are highly vulnerable to spoofing, data falsification, and cyber threats, which compromises air traffic management and poses significant challenges to UAV security. This paper presents an innovative approach to improving UAV security by introducing a novel steganographic method for ADS-B data protection. The proposed method leverages Fourier transformation to embed UAV identifiers into ADS-B signals, ensuring a high level of concealment and robustness against signal distortions. A key feature of the approach is the dynamic parameter management system, which adapts to varying transmission conditions to minimize distortions and enhance resilience. Experimental validation demonstrates that the method achieves a tenfold reduction in Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) compared to existing techniques such as mp3stego while also improving the Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR) compared to s-tools. The proposed solution ensures compliance with existing ADS-B standards, maintaining seamless integration with air traffic management systems while enhancing cybersecurity measures. By safeguarding UAV communications, the method contributes to the sustainable development of smart cities and supports critical applications such as logistics, environmental monitoring, and emergency response operations. These findings confirm the practical feasibility of the proposed approach and its potential to strengthen UAV security and ADS-B data protection, ultimately contributing to the resilience and sustainability of urban airspace infrastructure.

Suggested Citation

  • Serhii Semenov & Magdalena Krupska-Klimczak & Patryk Mazurek & Minjian Zhang & Olena Chernikh, 2025. "Improving Unmanned Aerial Vehicle Security as a Factor in Sustainable Development of Smart City Infrastructure: Automatic Dependent Surveillance–Broadcast (ADS-B) Data Protection," Sustainability, MDPI, vol. 17(4), pages 1-29, February.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1553-:d:1590565
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    References listed on IDEAS

    as
    1. Xuan Li & Yuanze Yang & Chuanwei Sun & Yong Fan, 2024. "Investigation, Evaluation, and Dynamic Monitoring of Traditional Chinese Village Buildings Based on Unmanned Aerial Vehicle Images and Deep Learning Methods," Sustainability, MDPI, vol. 16(20), pages 1-18, October.
    2. Jialiang Wang & Rui Tan & Liuyang Nie, 2022. "A Steady-State Flight Control Algorithm Combining Stretching Ratio Coefficient and PID Control for UAVs in Uncertain Environments," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    3. Lazaro J. Mangewa & Patrick A. Ndakidemi & Linus K. Munishi, 2019. "Integrating UAV Technology in an Ecological Monitoring System for Community Wildlife Management Areas in Tanzania," Sustainability, MDPI, vol. 11(21), pages 1-17, November.
    4. Runmin Zhao & Yi Huang & Haoyuan Luo & Xiaoming Huang & Yangzezhi Zheng, 2023. "A Framework for Using UAVs to Detect Pavement Damage Based on Optimal Path Planning and Image Splicing," Sustainability, MDPI, vol. 15(3), pages 1-26, January.
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