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- W2275741344 abstract "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<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M1><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incremental<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M2><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-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<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M3><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-means classification using Manhattan distance can classify network traffic 3 times faster than Euclidean distance at 671 thousands flow instances per second." @default.
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- W2275741344 date "2016-01-01" @default.
- W2275741344 modified "2023-09-27" @default.
- W2275741344 title "Online Incremental Learning for High Bandwidth Network Traffic Classification" @default.
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- W2275741344 doi "https://doi.org/10.1155/2016/1465810" @default.
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