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  4. Vol 8, Issue 5, 2024

The Application of Artificial Intelligence in Network Traffic Analysis and Prediction

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DOI: 10.23977/acss.2024.080509 | Downloads: 28 | Views: 1024

Author(s)

Shengnan Xu 1, Qianqian Wang 1

Affiliation(s)

1 Henan Vocational University of Science and Technology, Zhoukou, 466000, China

Corresponding Author

Shengnan Xu

ABSTRACT

Network security analysis plays a core position in the research field of detecting potential threats. At present, traffic analysis mainly relies on comparing behavior pattern information with predefined feature library, but this method faces the challenge of feature library structure foundation and updating lag. At the same time, due to the rapid evolution of the malicious attack strategy, it can often escape from the established detection rules, resulting in detection errors and omissions. Therefore, the intelligent security analysis system of network traffic is proposed and constructed. The system is committed to automatic learning of traffic characteristics, intelligent identification of abnormal behaviors, and in-depth analysis, which can improve the overall security and defense efficiency.

KEYWORDS

Artificial Intelligence; Traffic Detection; Abnormal Behavior; Self-Learning Model

CITE THIS PAPER

Shengnan Xu, Qianqian Wang, The Application of Artificial Intelligence in Network Traffic Analysis and Prediction. Advances in Computer, Signals and Systems (2024) Vol. 8: 80-86. DOI: http://dx.doi.org/10.23977/acss.2024.080509.

REFERENCES

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[2] Guo Shuanqi. Modeling and predictive analysis of massive network traffic with big data analysis technology [J]. Information Technology, 2021, (04): 102-106 + 112.
[3] Li Gang. Cluster analysis and wireless network traffic prediction of neural networks [J]. Modern Electronic Technology, 2021, 44 (07): 91-94.
[4] Li Xiaohui, Chen Chaoyang, Yi Huawei, Li Bo. Large-scale network traffic prediction based on cloud computing and big data analysis [J]. Journal of Jilin University (Engineering Edition), 2021, 51 (03): 1034-1039.
[5] Xu Huixiang, Cao Min, Ma Yingying. A combined prediction model for nonlinear network traffic based on big data analysis [J]. Journal of Shenyang University of Technology, 2020, 42 (06): 670-675.
[6] Tang Zhoujin, Peng Tao, Wang Wenbo. A local least-squares traffic prediction algorithm for small-scale network based on correlation analysis [J]. Journal of Physics, 2014, 63 (13): 57-66.

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