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- W4310349991 abstract "The complex problems of multiclass imbalance, virtual or real concept drift, concept evolution, high-speed traffic streams and limited label cost budgets pose severe challenges in network traffic classification tasks. In this paper, we propose a multiclass imbalanced and concept drift network traffic classification framework based on online active learning (MicFoal), which includes a configurable supervised learner for the initialization of a network traffic classification model, an active learning method with a hybrid label request strategy, a label sliding window group, a sample training weight formula and an adaptive adjustment mechanism for the label cost budget based on a periodic performance evaluation. In addition, a novel uncertain label request strategy based on a variable least confidence threshold vector is designed to address the problems of a variable multiclass imbalance ratio or even the number of classes changing over time. Experiments performed based on eight well-known real-world network traffic datasets demonstrate that MicFoal is more effective and efficient than several state-of-the-art learning algorithms." @default.
- W4310349991 created "2022-12-09" @default.
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- W4310349991 date "2023-01-01" @default.
- W4310349991 modified "2023-10-16" @default.
- W4310349991 title "Multiclass imbalanced and concept drift network traffic classification framework based on online active learning" @default.
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- W4310349991 doi "https://doi.org/10.1016/j.engappai.2022.105607" @default.
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