Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks
Network function virtualization (NFV) is a promising network paradigm that enables the design and implementation of novel network services with lower cost, increased agility, and faster time-to-value. However, network anomalies caused by software malfunction, hardware failure, mis-configuration, or...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8618315/ |
| _version_ | 1818855156367753216 |
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| author | Jing Chen Ming Chen Xianglin Wei Bing Chen |
| author_facet | Jing Chen Ming Chen Xianglin Wei Bing Chen |
| author_sort | Jing Chen |
| collection | DOAJ |
| description | Network function virtualization (NFV) is a promising network paradigm that enables the design and implementation of novel network services with lower cost, increased agility, and faster time-to-value. However, network anomalies caused by software malfunction, hardware failure, mis-configuration, or cyber attacks can greatly degrade the performance of NFV networks. A few matrix decomposition-based methods have shown their effectiveness in finding the existence of network-wide anomalies. However, a little attention has been paid to multiple anomalies detection and anomaly devices localization. To bridge this gap, in this paper, we propose a matrix differential decomposition (MDD)-based anomaly detection and localization algorithm for NFV networks. First, an NFV network prototype is built to investigate the property of NFV networks, and the effectiveness of traditional anomaly detection methods is evaluated. Second, we detail the MDD-based Anomaly DEtection and Localization (MADEL) algorithm. Finally, a series of experiments are conducted on three different NFV networks to evaluate the performance of the proposed algorithm. Experimental results show that the MADEL algorithm could effectively detect and localize different types of network anomalies. |
| first_indexed | 2024-12-19T08:04:07Z |
| format | Article |
| id | doaj.art-bc86ceb766f941058a07093dd650c082 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:04:07Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bc86ceb766f941058a07093dd650c0822022-12-21T20:29:47ZengIEEEIEEE Access2169-35362019-01-017293202933110.1109/ACCESS.2019.28936248618315Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV NetworksJing Chen0https://orcid.org/0000-0003-2672-4587Ming Chen1Xianglin Wei2Bing Chen3College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaNanjing Telecommunication Technology Research Institute, Nanjing, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaNetwork function virtualization (NFV) is a promising network paradigm that enables the design and implementation of novel network services with lower cost, increased agility, and faster time-to-value. However, network anomalies caused by software malfunction, hardware failure, mis-configuration, or cyber attacks can greatly degrade the performance of NFV networks. A few matrix decomposition-based methods have shown their effectiveness in finding the existence of network-wide anomalies. However, a little attention has been paid to multiple anomalies detection and anomaly devices localization. To bridge this gap, in this paper, we propose a matrix differential decomposition (MDD)-based anomaly detection and localization algorithm for NFV networks. First, an NFV network prototype is built to investigate the property of NFV networks, and the effectiveness of traditional anomaly detection methods is evaluated. Second, we detail the MDD-based Anomaly DEtection and Localization (MADEL) algorithm. Finally, a series of experiments are conducted on three different NFV networks to evaluate the performance of the proposed algorithm. Experimental results show that the MADEL algorithm could effectively detect and localize different types of network anomalies.https://ieeexplore.ieee.org/document/8618315/Network function virtualizationanomaly detectionlocalizationmatrix differential decomposition |
| spellingShingle | Jing Chen Ming Chen Xianglin Wei Bing Chen Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks IEEE Access Network function virtualization anomaly detection localization matrix differential decomposition |
| title | Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks |
| title_full | Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks |
| title_fullStr | Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks |
| title_full_unstemmed | Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks |
| title_short | Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks |
| title_sort | matrix differential decomposition based anomaly detection and localization in nfv networks |
| topic | Network function virtualization anomaly detection localization matrix differential decomposition |
| url | https://ieeexplore.ieee.org/document/8618315/ |
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