A Secure Deep Autoencoder-based 6G Channel Estimation to Detect/Mitigate Adversarial Attacks

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/27250

Title:	A Secure Deep Autoencoder-based 6G Channel Estimation to Detect/Mitigate Adversarial Attacks
Authors:	Oleiwi, HW Mhawi, DN Al-Raweshidy, HS
Keywords:	6G wireless communication networks;adversarial attacks;artificial intelligence;channel estimation;cybersecurity;deep autoencoder
Issue Date:	14-Jun-2023
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Citation:	Oleiwi, H.W., Mhawi, D.N. and Al-Raweshidy, H.S. (2023) 'A Secure Deep Autoencoder-based 6G Channel Estimation to Detect/Mitigate Adversarial Attacks', Proceedings - 2023 IEEE 5th Global Power, Energy and Communication Conference, GPECOM 2023, Nevsehir, Turkiye, 14-16 June, pp. 530 - 535. doi: 10.1109/GPECOM58364.2023.10175718.
URI:	https://bura.brunel.ac.uk/handle/2438/27250
DOI:	https://doi.org/10.1109/GPECOM58364.2023.10175718
ISBN:	979-8-3503-0198-4 (ebk) 979-8-3503-0199-1 (PoD)
ISSN:	2832-7667
Appears in Collections:	Dept of Electronic and Electrical Engineering Research Papers

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