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Design of Cybersecurity Threat Warning Model Based on Ant Colony Algorithm

by Weiwei Lin, Reiko Haga

1 School of Electronic and Information Engineering, Fujian Polytechnic Normal University, Fuqing, 350300, China
2 Engineering Research Center for ICH Digitalization and Multi-Source Information Fusion, Fujian Province University, Fuqing, 350300, China
3 CommScope Japan KK, Nagatacho, Tokyo, 100-0014, Japan

* Corresponding Author:Weiwei Lin. Email: email

Journal on Big Data 2021, 3(4), 147-153. https://doi.org/10.32604/jbd.2021.017299

Abstract

In this paper, a cybersecurity threat warning model based on ant colony algorithm is designed to strengthen the accuracy of the cybersecurity threat warning model in the warning process and optimize its algorithm structure. Through the ant colony algorithm structure, the local global optimal solution is obtained; and the cybersecurity threat warning index system is established. Next, the above two steps are integrated to build the cybersecurity threat warning model based on ant colony algorithm, and comparative experiment is also designed. The experimental results show that, compared with the traditional qualitative differential game-based cybersecurity threat warning model, the cybersecurity threat warning model based on ant colony algorithm has a higher correct rate in the warning process, and the algorithm program is simpler with higher use value.

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Cite This Article

APA Style
Lin, W., Haga, R. (2021). Design of cybersecurity threat warning model based on ant colony algorithm. Journal on Big Data, 3(4), 147-153. https://doi.org/10.32604/jbd.2021.017299
Vancouver Style
Lin W, Haga R. Design of cybersecurity threat warning model based on ant colony algorithm. J Big Data . 2021;3(4):147-153 https://doi.org/10.32604/jbd.2021.017299
IEEE Style
W. Lin and R. Haga, “Design of Cybersecurity Threat Warning Model Based on Ant Colony Algorithm,” J. Big Data , vol. 3, no. 4, pp. 147-153, 2021. https://doi.org/10.32604/jbd.2021.017299



cc Copyright © 2021 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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