A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction
With the rapid growth of data communications in size and complexity, the threat of malicious activities and computer crimes has increased accordingly as well. Thus, investigating efficient data processing techniques for network operation and management over large-scale network traffic is highly requ...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2014/323764 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563384608882688 |
|---|---|
| author | Liang Fu Lu Zheng-Hai Huang Mohammed A. Ambusaidi Kui-Xiang Gou |
| author_facet | Liang Fu Lu Zheng-Hai Huang Mohammed A. Ambusaidi Kui-Xiang Gou |
| author_sort | Liang Fu Lu |
| collection | DOAJ |
| description | With the rapid growth of data communications in size and complexity, the threat of malicious activities and computer crimes has increased accordingly as well. Thus, investigating efficient data processing techniques for network operation and management over large-scale network traffic is highly required. Some mathematical approaches on flow-level traffic data have been proposed due to the importance of analyzing the structure and situation of the network. Different from the state-of-the-art studies, we first propose a new decomposition model based on accelerated proximal gradient method for packet-level traffic data. In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed as NAD-APG. Based on the approach, we carry out the intrusion detection for packet-level network traffic data no matter whether it is polluted by noise or not. Finally, we design a prototype system for network anomalies detection such as Probe and R2L attacks. The experiments have shown that our approach is effective in revealing the patterns of network traffic data and detecting attacks from large-scale network traffic. Moreover, the experiments have demonstrated the robustness of the algorithm as well even when the network traffic is polluted by the large volume anomalies and noise. |
| format | Article |
| id | doaj-art-cbacfe9509614491b82e0d94c04ff096 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-cbacfe9509614491b82e0d94c04ff0962025-02-03T01:20:25ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2014-01-01201410.1155/2014/323764323764A Large-Scale Network Data Analysis via Sparse and Low Rank ReconstructionLiang Fu Lu0Zheng-Hai Huang1Mohammed A. Ambusaidi2Kui-Xiang Gou3Department of Mathematics, School of Science, Tianjin University, Tianjin 300072, ChinaDepartment of Mathematics, School of Science, Tianjin University, Tianjin 300072, ChinaFaculty of Engineering and IT, University of Technology, Sydney, NSW 2007, AustraliaDepartment of Mathematics, School of Science, Tianjin University, Tianjin 300072, ChinaWith the rapid growth of data communications in size and complexity, the threat of malicious activities and computer crimes has increased accordingly as well. Thus, investigating efficient data processing techniques for network operation and management over large-scale network traffic is highly required. Some mathematical approaches on flow-level traffic data have been proposed due to the importance of analyzing the structure and situation of the network. Different from the state-of-the-art studies, we first propose a new decomposition model based on accelerated proximal gradient method for packet-level traffic data. In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed as NAD-APG. Based on the approach, we carry out the intrusion detection for packet-level network traffic data no matter whether it is polluted by noise or not. Finally, we design a prototype system for network anomalies detection such as Probe and R2L attacks. The experiments have shown that our approach is effective in revealing the patterns of network traffic data and detecting attacks from large-scale network traffic. Moreover, the experiments have demonstrated the robustness of the algorithm as well even when the network traffic is polluted by the large volume anomalies and noise.http://dx.doi.org/10.1155/2014/323764 |
| spellingShingle | Liang Fu Lu Zheng-Hai Huang Mohammed A. Ambusaidi Kui-Xiang Gou A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction Discrete Dynamics in Nature and Society |
| title | A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction |
| title_full | A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction |
| title_fullStr | A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction |
| title_full_unstemmed | A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction |
| title_short | A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction |
| title_sort | large scale network data analysis via sparse and low rank reconstruction |
| url | http://dx.doi.org/10.1155/2014/323764 |
| work_keys_str_mv | AT liangfulu alargescalenetworkdataanalysisviasparseandlowrankreconstruction AT zhenghaihuang alargescalenetworkdataanalysisviasparseandlowrankreconstruction AT mohammedaambusaidi alargescalenetworkdataanalysisviasparseandlowrankreconstruction AT kuixianggou alargescalenetworkdataanalysisviasparseandlowrankreconstruction AT liangfulu largescalenetworkdataanalysisviasparseandlowrankreconstruction AT zhenghaihuang largescalenetworkdataanalysisviasparseandlowrankreconstruction AT mohammedaambusaidi largescalenetworkdataanalysisviasparseandlowrankreconstruction AT kuixianggou largescalenetworkdataanalysisviasparseandlowrankreconstruction |