Hybrid-enhanced secure strategy for encryption image and key management based on WDM/TDM-PON using hill group cipher
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Atheer J. Mansoor
,
Mohammed Mejbel Salih
and
Hayder H. Al-khaylani
Abstract
Information and communication technology (ICT) has advanced significantly in the modern era, and the majority of smart technology utilized in smart cities are Internet of things (IoT) based. Hybrid passive optical networks (PON) consist of wavelength division multiplexing (WDM) and time division multiplexing (TDM) are of the most encouraging and widely utilized technologies in optical distribution networks (ODN) required for security, encryption data and bandwidth. The security of the mystery/private cryptographic key is crucial to the encryption process’s ability to secure data. Any strong encryption algorithm would be compromised by poor key management. In order to enhance the physical security, so as to protect the secret data during transmission across the channel and to ensure a secure exchange of the encryption key between the transmitter and the recipient, a hybrid system that incorporates cryptography, steganography, and key hiding techniques has been employed. To the best of our knowledge, Opti-System software with python devices combine to create a secure WDM/TDM-PON system depending on the Hill Group cipher and ITU-T G.989.2 standards. Simulation studies indicate that, a bidirectional fiber distance with a splitting ratio 1:64 and a symmetrical 40 Gbps can be accomplished successfully out at 60 km. The least amount of acceptable receiver sensitivity is −28.1 dBm for uplink (U/S) and −23.7 dBm for downlink (D/S).
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Research ethics: “The local Institutional Review Board deemed the study exempt from review” if the IRB specifically exempted the study from review.
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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