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- W4386323463 abstract "With the rapid progress of six-generation (6G) wireless communication technologies, Low Earth Orbit (LEO) satellite global network technology played a very important role and promoted millimeter-wave (mm-wave) systems gained demand. However, security remains a critical concern in wireless network design and usage. In this paper, we propose a method based on an artificial intelligence technique, machine-learning algorithms in cybersecurity. This approach handles significant security dataset training, using different techniques such as decision trees and Recurrent Neural networks, which allow feature selection and Sybil attack classification. In addition, we refer to the guidelines and specifications of orbit satellite standards in ISO 16164-2015 and ISO 27852-16 to validate the satellite transmitter. Simulation results arise an improvement in security and the ability to detect and prevent Sybil attacks. Overall, this approach makes a significant contribution to the field of satellite cybersecurity by providing a standard-compliant solution" @default.
- W4386323463 created "2023-09-01" @default.
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- W4386323463 date "2023-07-17" @default.
- W4386323463 modified "2023-10-08" @default.
- W4386323463 title "Sybil Attack Models of MM-Wave Communication for LEO Satellite Network in Cybersecurity" @default.
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- W4386323463 doi "https://doi.org/10.1109/icce-taiwan58799.2023.10226952" @default.
- W4386323463 hasPublicationYear "2023" @default.
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