Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/28842
Autoria: | Viana, J. Farkhari, H. Campos, L. M. Sebastião, P. Cercas, F. Bernardo, L. Dinis, R. |
Editor: | Hämäläinen, J. |
Data: | 2022 |
Título próprio: | Two methods for jamming identification in UAV networks using new synthetic dataset |
Título e volume do livro: | 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring) |
Título do evento: | 95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring |
Referência bibliográfica: | Viana, J., Farkhari, H., Campos, L. M., Sebastião, P., Cercas, F., Bernardo, L., & Dinis, R. (2022). Two methods for jamming identification in UAV networks using new synthetic dataset. In J. Hämäläinen (Ed.), 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring). IEEE. https://doi.org/10.1109/VTC2022-Spring54318.2022.9860816 |
ISSN: | 1090-3038 |
ISBN: | 978-1-6654-8243-1 |
DOI (Digital Object Identifier): | 10.1109/VTC2022-Spring54318.2022.9860816 |
Palavras-chave: | Cybersecurity Convolutional Neural Networks (CNNs) Deep learning Jamming detection Jamming identification UAV Unmanned Aerial Vehicles 4G 5G |
Resumo: | Unmanned aerial vehicle (UAV) systems are vulnerable to jamming from self-interested users who utilize radio devices to disrupt UAV transmissions. The vulnerability occurs due to the open nature of air-to-ground (A2G) wireless communication networks, which may enable network-wide attacks. This paper presents two strategies to identify Jammers in UAV networks. The first strategy is based on a time series approach for anomaly detection where the available signal in the resource block is decomposed statistically to find trends, seasonality, and residues. The second is based on newly designed deep networks. The combined techniques are suitable for UAVs because the statistical model does not require heavy computation processing, but is limited to generalizing possible attacks that might occur. On the other hand, the designed deep network can classify attacks accurately, but requires more resources. The simulation considers the location and power of the jamming attacks and the UAV position related to the base station. The statistical method technique made it feasible to identify 84.38% of attacks when the attacker was at a distance of 30 m from the UAV. Furthermore, the Deep network’s accuracy was approximately 99.99 % for jamming powers greater than two and jammer distances less than 200 meters. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | IT-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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conferenceobject_89477.pdf | 1,67 MB | Adobe PDF | Ver/Abrir |
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