A Review of AI-Based Network Anomaly Traffic Detection

Kehan Li

doi:10.47297/taposatWSP2633-456919.20250608

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A Review of AI-Based Network Anomaly Traffic Detection

6卷第8期

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A Review of AI-Based Network Anomaly Traffic Detection
A Review of AI-Based Network Anomaly Traffic Detection
School of Information Engineering, Sichuan Agricultural University, Chengdu, Sichuan, 611230, China
2025年6卷第8期页码：100-104
纸质出版：2025-08-30
DOI：10.47297/taposatWSP2633-456919.20250608
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(2025). A Review of AI-Based Network Anomaly Traffic Detection. 科技理论与实践（英文版）, 6(8), 100-104.

Li Kehan.A Review of AI-Based Network Anomaly Traffic Detection[J].Theory and Practice of Science and Technology,2025,06(08):100-104.
(2025). A Review of AI-Based Network Anomaly Traffic Detection. 科技理论与实践（英文版）, 6(8), 100-104. DOI： 10.47297/taposatWSP2633-456919.20250608.

Li Kehan.A Review of AI-Based Network Anomaly Traffic Detection[J].Theory and Practice of Science and Technology,2025,06(08):100-104. DOI： 10.47297/taposatWSP2633-456919.20250608.

摘要

Abstract

In the face of increasingly complex and covert cyberattacks

cybersecurity has become paramount

yet traditional methods face limitations. Consequently

AI-driven anomaly traffic detection has emerged as a hot research topic in the field. This review systematically organizes research progress in AI-based network anomaly traffic detection. It first defines anomaly traffic types and analyzes limitations of traditional detection methods

then focuses on key technologies of machine learning and deep learning models in feature learning and anomaly identification. The review compares the advantages and disadvantages of different models

finally analyzing current challenges and outlining future research directions.

关键词

Keywords

references

Zhu ,R. (2024)Fast identification of abnormal traffic in communication networks based on deep learning. Yangtze River Information and Communication.,37(12):96-98.

Xu,Y.(2025)Network anomaly traffic detection method based on spatio-temporal feature fusion. Electronic Measurement Technology., 48(14): 19-25.

Wei, B. (2025)Anomalous behavior detection method for large-scale network traffic based on LSTM. Industrial Control Computer.,38(08):89-91.

Deng, H. W.. (2023)Deep learning-based network traffic anomaly identification and detection. Computer System Applications.,32(02):274-280.

Zou,X. (2025)Anomaly identification model of supply chain data based on improved adversarial network. Computer Applications and Software., 42(07): 107-110.

Zhang,R . (2024)Decision tree mining based anomaly identification in mobile communication networks. Telecommunications Express.,(10):36-39.

Li, C . (2024)Automatic identification of anomalous traffic in resilient optical networks based on isolated forest algorithm. Laser Journal., 45(01): 179-183.

Yuan, Q. (2024)Network traffic anomaly identification and detection based on RF+TabNet. Network Security Technology and Application., (12): 38-42.

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