Online Incremental Learning for High Bandwidth Network Traffic Classification
Data stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2016/1465810 |
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| _version_ | 1832566859020369920 |
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| author | H. R. Loo S. B. Joseph M. N. Marsono |
| author_facet | H. R. Loo S. B. Joseph M. N. Marsono |
| author_sort | H. R. Loo |
| collection | DOAJ |
| description | Data stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such as Field Programmable Gate Array (FPGA). This paper proposes an algorithm for online network traffic classification based on the concept of incremental k-means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incremental k-means (Euclidean and Manhattan) distance are analyzed to measure their impact on the network traffic classification in the presence of concept drift. The experimental results on real datasets show that the proposed algorithm exhibits consistency, up to 94% average accuracy for both distance measures, even in the presence of concept drifts. The proposed incremental k-means classification using Manhattan distance can classify network traffic 3 times faster than Euclidean distance at 671 thousands flow instances per second. |
| format | Article |
| id | doaj-art-e0ee7bd3a9ff4681bd94a0d23d1c368e |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-e0ee7bd3a9ff4681bd94a0d23d1c368e2025-02-03T01:03:10ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322016-01-01201610.1155/2016/14658101465810Online Incremental Learning for High Bandwidth Network Traffic ClassificationH. R. Loo0S. B. Joseph1M. N. Marsono2Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, MalaysiaFaculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, MalaysiaFaculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, MalaysiaData stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such as Field Programmable Gate Array (FPGA). This paper proposes an algorithm for online network traffic classification based on the concept of incremental k-means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incremental k-means (Euclidean and Manhattan) distance are analyzed to measure their impact on the network traffic classification in the presence of concept drift. The experimental results on real datasets show that the proposed algorithm exhibits consistency, up to 94% average accuracy for both distance measures, even in the presence of concept drifts. The proposed incremental k-means classification using Manhattan distance can classify network traffic 3 times faster than Euclidean distance at 671 thousands flow instances per second.http://dx.doi.org/10.1155/2016/1465810 |
| spellingShingle | H. R. Loo S. B. Joseph M. N. Marsono Online Incremental Learning for High Bandwidth Network Traffic Classification Applied Computational Intelligence and Soft Computing |
| title | Online Incremental Learning for High Bandwidth Network Traffic Classification |
| title_full | Online Incremental Learning for High Bandwidth Network Traffic Classification |
| title_fullStr | Online Incremental Learning for High Bandwidth Network Traffic Classification |
| title_full_unstemmed | Online Incremental Learning for High Bandwidth Network Traffic Classification |
| title_short | Online Incremental Learning for High Bandwidth Network Traffic Classification |
| title_sort | online incremental learning for high bandwidth network traffic classification |
| url | http://dx.doi.org/10.1155/2016/1465810 |
| work_keys_str_mv | AT hrloo onlineincrementallearningforhighbandwidthnetworktrafficclassification AT sbjoseph onlineincrementallearningforhighbandwidthnetworktrafficclassification AT mnmarsono onlineincrementallearningforhighbandwidthnetworktrafficclassification |