Multiclass imbalanced and concept drift network traffic classification framework based on online active learning

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 ulticlass mbalanced and oncept drift network traffic classification ramework based on nline ctive earning (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.