Unsupervised Anomaly Detection for Rural Fixed Wireless LTE Networks

被引：0

作者：

Colpitts, Alexander G. B. ^{[1
]}

Petersen, Brent R. ^{[1
]}

机构：

[1] Univ New Brunswick, Dept Elect & Comp Engn, Fredericton, NB E3B 5A3, Canada

来源：

IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING | 2023年 / 46卷 / 04期

关键词：

Intelligent networks; network fault diagnosis; rural areas; unsupervised learning;

D O I：

10.1109/ICJECE.2023.3275975

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article presents an anomaly detection (AD) algorithm, robust AD for rural fixed wireless LTE (RAINFOREST), to address the difficulty of fault detection in LTE networks, specifically those that are rural and fixed wireless. We propose a hybrid AD method that uses network key performance indicators (KPIs), historical KPI forecasts, density-based spatial clustering of applications with noise (DBSCAN), and statistical analysis to detect anomalies. RAINFOREST outperformed benchmark AD methods and was able to detect faults in a rural commercial fixed wireless network earlier than existing LTE threshold-based alarms.

引用

页码：256 / 261

页数：6

共 50 条

[21] Unsupervised Learning for Anomaly Detection of Electric Motors [J].

Son, Jonghwan ;

Kim, Chayoung ;

Jeong, Minjoong .

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 2022, 23 (04) :421-427

[22] AutoAD: an Automated Framework for Unsupervised Anomaly Detection [J].

Putina, Andrian ;

Bahri, Maroua ;

Salutari, Flavia ;

Sozio, Mauro .

2022 IEEE 9TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA), 2022, :106-115

[23] Unsupervised Deep Anomaly Detection in Chest Radiographs [J].

Nakao, Takahiro ;

Hanaoka, Shouhei ;

Nomura, Yukihiro ;

Murata, Masaki ;

Takenaga, Tomomi ;

Miki, Soichiro ;

Watadani, Takeyuki ;

Yoshikawa, Takeharu ;

Hayashi, Naoto ;

Abe, Osamu .

JOURNAL OF DIGITAL IMAGING, 2021, 34 (02) :418-427

[24] Anomaly Detection by Recombining Gated Unsupervised Experts [J].

Schulze, Jan-Philipp ;

Sperl, Philip ;

Boettinger, Konstantin .

2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022,

[25] Unsupervised motion-based anomaly detection with graph attention networks for industrial robots labeling [J].

Han, Jinrui ;

Chen, Zhen ;

Zhou, Di ;

Hu, Bing ;

Xia, Tangbin ;

Pan, Ershun .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2025, 146

[26] An unsupervised anomaly detection framework for smart assisted living via growing neural gas networks [J].

Ciprian, Matteo ;

Gadaleta, Matteo ;

Rossi, Michele .

JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, 2024, 16 (03) :365-387

[27] The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection [J].

Bergmann, Paul ;

Batzner, Kilian ;

Fauser, Michael ;

Sattlegger, David ;

Steger, Carsten .

INTERNATIONAL JOURNAL OF COMPUTER VISION, 2021, 129 (04) :1038-1059

[28] The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection [J].

Paul Bergmann ;

Kilian Batzner ;

Michael Fauser ;

David Sattlegger ;

Carsten Steger .

International Journal of Computer Vision, 2021, 129 :1038-1059

[29] Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection [J].

Beil, Yuanchen ;

Zhoult, Sheng ;

Tan, Qiaoyu ;

Xu, Hao ;

Chen, Hao ;

Li, Zhao ;

But, Jiajun .

23RD IEEE INTERNATIONAL CONFERENCE ON DATA MINING, ICDM 2023, 2023, :11-20

[30] Online and Scalable Unsupervised Network Anomaly Detection Method [J].

Dromard, Juliette ;

Roudiere, Gilles ;

Owezarski, Philippe .

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2017, 14 (01) :34-47

← 1 2 3 4 5 →