A semi-supervised learning based approach for network anomalous traffic detection
Addressing the challenges such as difficulty in obtaining labeled samples and class imbalance in network traffic data,a semi-supervised approach with augmented synth data for anomalous network traffic detection called SEASAND was proposed.Leveraging unlabeled data for model learning,SEASAND achieved high identification accuracy with minimal labeled data,thereby reducing training costs.The method incorporates consistency regularization and entropy minimization principles,addressing the issue of imbalanced network traffic data through mixed sampling.Additionally,a hybrid sample algorithm is employed to augment the samples,enhancing the utilization efficiency of unlabeled data.The augmented dataset is then trained using the one-dimensional residual network Resnet1D-18.The simulation experimental results on KDDCup99-10,UNSW-NB15,and CICIDS2017 datasets show that SEASAND outperforms related algorithms in the context of few-shot multi-class classification,thereby reducing the demand for labeled samples.
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