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NADS-RA: Network Anomaly Detection Scheme Based on feature Representation and data Augmentation
2020
IEEE Access
Network anomaly detection aims to identify network anomalies, and it has obtained many achievements using the supervised classification technique. Since the supervised classifier depends on the prior data, it is difficult to accurately classify the rare anomalies when they account less in the training set. Data augmentation can tackle the imbalanced training set problem through creating artificial rare anomaly samples. However, the existing data augmentation methods either ignore the data
doi:10.1109/access.2020.3040510
fatcat:muqfbnj6kragdg5ui36afajxuy