Structured-Anomaly Pursuit of Network Traffic via Corruption-Robust Low-Rank Tensor Decomposition

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成果类型：

期刊论文

作者：

Zeng, Jiuzhen;Yang, Laurence T.;Wang, Chao;Ruan, Yiheng;Zhu, Chenlu

通讯作者：

Yang, LT

作者机构：

[Zeng, Jiuzhen; Ruan, Yiheng; Yang, Laurence T.; Zhu, Chenlu] Huazhong Univ Sci & Technol, Hubei Engn Res Ctr Big Data Secur, Sch Cyber Sci & Engn, Hubei Key Lab Distributed Syst Secur, Wuhan 430074, Peoples R China.

[Zeng, Jiuzhen; Wang, Chao] Univ South China, Sch Elect Engn, Hengyang 421001, Peoples R China.

[Yang, Laurence T.] Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou 450001, Peoples R China.

[Yang, Laurence T.] St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS B2G 2W5, Canada.

[Ruan, Yiheng] Hubei Chutian Smart Commun Co Ltd, Wuhan 430074, Peoples R China.

通讯机构：

[Yang, LT ] H

Huazhong Univ Sci & Technol, Hubei Engn Res Ctr Big Data Secur, Sch Cyber Sci & Engn, Hubei Key Lab Distributed Syst Secur, Wuhan 430074, Peoples R China.

Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou 450001, Peoples R China.

语种：

英文

关键词：

Network traffic;anomaly pursuit;low-rank decomposition;tensor theories and methods

期刊：

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING

ISSN：

2327-4697

年：

2024

卷：

期：

页码：

2510-2523

DOI：

10.1109/TNSE.2023.3277895

基金类别：

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61932010) National Key R&D Program of China (Grant Number: 2022YFE0138600) Scientific Research Fund of Hunan Provincial Education Department (Grant Number: 20C1619)

机构署名：

本校为其他机构

院系归属：

电气工程学院

摘要：

Accurately pursuing network traffic anomalies is crucial to network maintenance and management. However, existing methods generally focus on detecting uniformly distributed sparse noises and therefore fail to deal with non-uniform or sequential anomalies with effect. In this article, a novel corruption-robust low-rank tensor decomposition (Cr-LTD) method is proposed for accurate and efficient structured-anomaly pursuit even in presence of sparse corruptions. For the intrinsically low-rank network traffic observation, Cr-LTD models it as a three-way tensor and formulates the traffic anomaly pur...

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Structured-Anomaly Pursuit of Network Traffic via Corruption-Robust Low-Rank Tensor Decomposition

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