Research on Cybersecurity Talent Assessment Model Based on Blockchain Technology and Neural Network
- DOI
- 10.2991/978-94-6463-504-1_25How to use a DOI?
- Keywords
- blockchain; neural network; cybersecurity talent; consensus mechanisms
- Abstract
This paper proposes a cybersecurity talent assessment model based on blockchain and neural network, aiming to realize comprehensive, efficient and secure supervision of cybersecurity talents. The model is centered on blockchain technology, and a secure and trustworthy cybersecurity talent information base is constructed using a federation chain. The traditional DPoS consensus mechanism is improved and a neural network algorithm is introduced for evaluating and predicting the ability, behavior, and reputation of talents to provide a scientific basis for regulation. The experimental results for the improved consensus mechanism and neural network show that the model is able to realize comprehensive supervision of cybersecurity talents, improve the efficiency and accuracy of supervision, and provide strong support for the cultivation and management of cybersecurity talents.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jun Zhao AU - Chen Zhou PY - 2024 DA - 2024/08/31 TI - Research on Cybersecurity Talent Assessment Model Based on Blockchain Technology and Neural Network BT - Proceedings of the 2024 3rd International Conference on Information Economy, Data Modelling and Cloud Computing (ICIDC 2024) PB - Atlantis Press SP - 257 EP - 266 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-504-1_25 DO - 10.2991/978-94-6463-504-1_25 ID - Zhao2024 ER -