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Which standard classification algorithm has more stable performance for imbalanced network traffic data?
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DOI：10.1007/s00500-023-09331-1.png)
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Abstract
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Most standard classification algorithms are difficult to effectively learn and predict from imbalanced network traffic data, which usually leads to lower classification accuracy. To analyze the influence of imbalanced network traffic data on the performance of standard classification algorithms, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced network traffic data set with gradually varying Imbalance Ratio (IR) and belonging to the same distribution. Then, to obtain more objective classification result and simplify the evaluation process, the evaluation metric AFG is used to evaluate the classification performance of standard classification algorithms based on area under the receiver operating characteristic curve (AUC), F-measure and G-mean. Finally, based on AFG and coefficient of variation (CV), performance stability of standard classification algorithms on imbalanced network traffic data is obtained. Experiments of eight widely used standard classification algorithms on 25 different imbalanced network traffic data demonstrate that the classification performance of GNB, RF and DT is unstable, while BNB, KNN, LR, GBDT, and SVC are relatively stable and not susceptible to imbalanced data. Especially, the KNN has the most stable classification performance. Also, the results are statistically confirmed by Friedman and Nemenyi post hoc statistical tests.
Keywords:
Imbalanced network traffic data
Data augmentation algorithms
Standard classification algorithms
Stable classification performance
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