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Enhancing network security based on 10G-EPON with the use of the Hill cipher algorithm

  • Ekhlass Mousa , Essam N. Abdulla EMAIL logo and Salah A. Adnan
Published/Copyright: July 8, 2025
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Abstract

As the necessity for critical network operations has grown dramatically, security concerns with optical access networks are becoming increasingly important. This study evaluates the security of self-protected long-distance 10 Gbps Ethernet passive optical networks (10G-EPON), which aims at solving some of the limitations of both networks and offers a creative, robust alternative. In accordance with the institute of electrical and electronics engineers (IEEE 802.3 av) requirements series, the solution permits secure mutual identification and key management In an optical distribution network (ODN), optical line terminals (OLTs) are connected to optical network units (ONUs). 10G-EPON with full services can provide consumers with effective gigabit transmission with ensured quality of service (QoS) in a variety of FTTx situations (fiber-to-the-premises/node/curb/business/users/home). The only equipment it uses is passive, with the exception of the central office and customer facilities. A secure 10G-EPON network with an optimum link distance of 75 km can support 32 splitting ratio with an optimum level of BER roughly 10−9 and a minimal allowable quality factor of 6. The optimum distance link between optical successive network design terminals is 20 km as per IEEE 802.3 av specification. 10G-EPON can transmit data at symmetric 10 Gbps upstream (U/S) and downstream (D/S) with a minimum permissible receiving sensitivity of −34.6 dB for direction of U/S and −32 dBm for direction of D/S.


Corresponding author: Essam N. Abdulla, Optoelectronics Engineering Branch, Laser and Optoelectronics Department, University of Technology–Iraq, Baghdad, 10021, Iraq, E-mail:

  1. Research ethics: “The local Institutional Review Board deemed the study exempt from review” if the IRB specifically exempted the study from review.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-05-22
Accepted: 2025-06-16
Published Online: 2025-07-08

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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