ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

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A Framework for Improving the Performance of QKDN using Machine Learning Approach

  • R Arthi
    Faculty of Engineering and Technology, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai arthir2[at]srmist.edu.in
  • A Saravanan
    Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai, India
  • J S Nayana
    Faculty of Engineering and Technology, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai
  • Chandresh MuthuKumaran
    Faculty of Engineering and Technology, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai
https://doi.org/10.14201/adcaij.30240

Resumen

A reliable secure communication can be given between two remote parties by key sharing, quantum key distribution (QKD) is widely concentrated as the information in QKD is safeguarded by the laws of quantum physics. There are many techniques that deal with quantum key distribution network (QKDN), however, only few of them use machine learning (ML) and soft computing techniques to improve QKDN. ML can analyze data and improve itself through model training without having to be programmed manually. There has been a lot of progress in both the hardware and software of ML technologies. Given ML’s advantageous features, it can help improve and resolve issues in QKDN, facilitating its commercialization. The proposed work provides a detailed understanding of role of each layer of QKDN, addressing the limitations of each layer, and suggesting a framework to improve the performance metrics for various applications of QKDN by applying machine learning techniques, such as support vector machine and decision tree algorithms.
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Arthi, R., Saravanan, A., Nayana, J. S., & MuthuKumaran, C. (2023). A Framework for Improving the Performance of QKDN using Machine Learning Approach. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e30240. https://doi.org/10.14201/adcaij.30240

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