Detecção de Ataques de GPS em Veículos Aéreos Não Tripulados com Classificação Multiclasse
Resumo
Veículos aéreos não tripulados (VANTs) têm sido cada vez mais utilizados em diversos domínios. Esses veículos geralmente dependem do Sistema de Posicionamento Global (GPS), o que os torna vulneráveis a ataques baseados em sinais de GPS falsos. Assim, este artigo propõe um Sistema de Detecção de Intrusão (IDS) que utiliza técnicas de aprendizado de máquina para detectar e identificar GPS Jamming e três tipos de ataques de GPS Spoofing. O classificador multiclasse proposto permite a identificação do tipo de ataque – algo essencial para determinar as medidas de proteção mais eficazes. A acurácia alcançada foi de 98,08%, com 2,6% de falsos negativos, diminuindo a probabilidade de ignorar ataques, algo essencial em infraestruturas com VANTs reais.
Referências
Aissou, G., Benouadah, S., El Alami, H., and Kaabouch, N. (2022). Instance-based supervised machine learning models for detecting GPS spoofing attacks on UAS. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), pages 0208–0214.
Aissou, G., Slimane, H. O., Benouadah, S., and Kaabouch, N. (2021). 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), pages 0649–0653.
Bauernfeind, R., Kraus, T., Dötterböck, D., Eissfeller, B., Loehnert, E., and Wittmann, E. (2011). Car jammers: Interference analysis. GPS World, 22:28–35.
Burkov, A. (2019). The Hundred-Page Machine Learning Book. Andriy Burkov, Quebec.
da Silva, D. A. M. (2017). Gps jamming and spoofing using software defined radio. Master’s thesis, University Institute of Lisbon.
da Silva, R. A. C., da Fonseca, N. L. S., and Boutaba, R. (2021). Evaluation of the employment of UAVs as fog nodes. IEEE Wireless Communications, 28(5):20–27.
Davidovich, B., Nassi, B., and Elovici, Y. (2022). Towards the detection of GPS spoofing attacks against drones by analyzing camera’s video stream. Sensors, 22(7).
Derhab, A., Cheikhrouhou, O., Allouch, A., Koubaa, A., Qureshi, B., Ferrag, M. A., Maglaras, L., and Khan, F. A. (2023). Internet of drones security: Taxonomies, open issues, and future directions. Vehicular Communications, 39:100552.
Gasimova, A., Khoei, T. T., and Kaabouch, N. (2022). A comparative analysis of the ensemble models for detecting GPS spoofing attacks on UAVs. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), pages 0310–0315.
Géron, A. (2023). Hands-On Machine Learning with ScikitLearn, Keras, and Tensor-Flow. O’Reilly Media, Sebastopol.
Haider, Z. and Khalid, S. (2016). Survey on effective GPS spoofing countermeasures. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH), pages 573–577.
Khan, A., Gupta, S., and Gupta, S. K. (2022). Emerging UAV technology for disaster detection, mitigation, response, and preparedness. Journal of Field Robotics, 39(6):905–955.
Khoei, T. T., Aissou, G., Al Shamaileh, K., Devabhaktuni, V. K., and Kaabouch, N. (2023). Supervised deep learning models for detecting GPS spoofing attacks on unmanned aerial vehicles. In 2023 IEEE International Conference on Electro Information Technology (eIT), pages 340–346.
Khoei, T. T., Gasimova, A., Ahajjam, M. A., Shamaileh, K. A., Devabhaktuni, V., and Kaabouch, N. (2022). A comparative analysis of supervised and unsupervised models for detecting GPS spoofing attack on UAVs. In 2022 IEEE International Conference on Electro Information Technology (eIT), pages 279–284.
Lemos, G. (2023). Original dataset. [link].
Misra, P. and Enge, P. (2006). Global Position System Signals, Measurement and Performance. Ganga Jamuna Press, Lincoln.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Ranyal, E. and Jain, K. (2021). Unmanned aerial vehicle’s vulnerability to GPS spoofing a review. Indian Society of Remote Sensing, 49:585–591.
Talaei Khoei, T., Ismail, S., and Kaabouch, N. (2022). Dynamic selection techniques for detecting GPS spoofing attacks on UAVs. Sensors, 22(2).
Talaei Khoei, T., Ismail, S., Shamaileh, K. A., Devabhaktuni, V. K., and Kaabouch, N. (2023). Impact of dataset and model parameters on machine learning performance for the detection of GPS spoofing attacks on unmanned aerial vehicles. Applied Sciences, 13(1).
Wei, X., Wang, Y., and Sun, C. (2022). Perdet: Machine-learning-based UAV GPS spoofing detection using perception data. Remote Sensing, 14(19).
Whelan, J., Almehmadi, A., and El-Khatib, K. (2022). Artificial intelligence for intrusion detection systems in unmanned aerial vehicles. Computers and Electrical Engineering, 99:107784.
Wolpert, D. H. (1996). The Lack of A Priori Distinctions Between Learning Algorithms. Neural Computation, 8(7):1341–1390.
Aissou, G., Slimane, H. O., Benouadah, S., and Kaabouch, N. (2021). 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), pages 0649–0653.
Bauernfeind, R., Kraus, T., Dötterböck, D., Eissfeller, B., Loehnert, E., and Wittmann, E. (2011). Car jammers: Interference analysis. GPS World, 22:28–35.
Burkov, A. (2019). The Hundred-Page Machine Learning Book. Andriy Burkov, Quebec.
da Silva, D. A. M. (2017). Gps jamming and spoofing using software defined radio. Master’s thesis, University Institute of Lisbon.
da Silva, R. A. C., da Fonseca, N. L. S., and Boutaba, R. (2021). Evaluation of the employment of UAVs as fog nodes. IEEE Wireless Communications, 28(5):20–27.
Davidovich, B., Nassi, B., and Elovici, Y. (2022). Towards the detection of GPS spoofing attacks against drones by analyzing camera’s video stream. Sensors, 22(7).
Derhab, A., Cheikhrouhou, O., Allouch, A., Koubaa, A., Qureshi, B., Ferrag, M. A., Maglaras, L., and Khan, F. A. (2023). Internet of drones security: Taxonomies, open issues, and future directions. Vehicular Communications, 39:100552.
Gasimova, A., Khoei, T. T., and Kaabouch, N. (2022). A comparative analysis of the ensemble models for detecting GPS spoofing attacks on UAVs. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), pages 0310–0315.
Géron, A. (2023). Hands-On Machine Learning with ScikitLearn, Keras, and Tensor-Flow. O’Reilly Media, Sebastopol.
Haider, Z. and Khalid, S. (2016). Survey on effective GPS spoofing countermeasures. In 2016 Sixth International Conference on Innovative Computing Technology (INTECH), pages 573–577.
Khan, A., Gupta, S., and Gupta, S. K. (2022). Emerging UAV technology for disaster detection, mitigation, response, and preparedness. Journal of Field Robotics, 39(6):905–955.
Khoei, T. T., Aissou, G., Al Shamaileh, K., Devabhaktuni, V. K., and Kaabouch, N. (2023). Supervised deep learning models for detecting GPS spoofing attacks on unmanned aerial vehicles. In 2023 IEEE International Conference on Electro Information Technology (eIT), pages 340–346.
Khoei, T. T., Gasimova, A., Ahajjam, M. A., Shamaileh, K. A., Devabhaktuni, V., and Kaabouch, N. (2022). A comparative analysis of supervised and unsupervised models for detecting GPS spoofing attack on UAVs. In 2022 IEEE International Conference on Electro Information Technology (eIT), pages 279–284.
Lemos, G. (2023). Original dataset. [link].
Misra, P. and Enge, P. (2006). Global Position System Signals, Measurement and Performance. Ganga Jamuna Press, Lincoln.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Ranyal, E. and Jain, K. (2021). Unmanned aerial vehicle’s vulnerability to GPS spoofing a review. Indian Society of Remote Sensing, 49:585–591.
Talaei Khoei, T., Ismail, S., and Kaabouch, N. (2022). Dynamic selection techniques for detecting GPS spoofing attacks on UAVs. Sensors, 22(2).
Talaei Khoei, T., Ismail, S., Shamaileh, K. A., Devabhaktuni, V. K., and Kaabouch, N. (2023). Impact of dataset and model parameters on machine learning performance for the detection of GPS spoofing attacks on unmanned aerial vehicles. Applied Sciences, 13(1).
Wei, X., Wang, Y., and Sun, C. (2022). Perdet: Machine-learning-based UAV GPS spoofing detection using perception data. Remote Sensing, 14(19).
Whelan, J., Almehmadi, A., and El-Khatib, K. (2022). Artificial intelligence for intrusion detection systems in unmanned aerial vehicles. Computers and Electrical Engineering, 99:107784.
Wolpert, D. H. (1996). The Lack of A Priori Distinctions Between Learning Algorithms. Neural Computation, 8(7):1341–1390.
Publicado
16/09/2024
Como Citar
LEMOS, Gustavo Gualberto Rocha de; SILVA, Rodrigo Augusto Cardoso da.
Detecção de Ataques de GPS em Veículos Aéreos Não Tripulados com Classificação Multiclasse. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 24. , 2024, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 210-225.
DOI: https://doi.org/10.5753/sbseg.2024.241445.
