Combating imbalance in network intrusion datasets

By David A. Cieslak, Nitesh V. Chawla, Aaron D. Striegel. IEEE International Conference on Granular Computing, 2006

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Oversampling for Imbalanced Learning Based on K-Means and SMOTE (2017, background): “This is in contrast to cluster-SMOTE, where only minority class instances are clustered [7] and to the aforementioned combination of oversampling and undersampling where both classes are clustered separately [44] .”
A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining (2023, unknown): “Similarly, some variants of SMOTE such as cluster SMOTE or borderline SMOTE [22,23] and nearest-neighbor-based undersampling techniques such as ENN [24] were also found to work well for datasets with a low to moderate imbalance ratio, while the other SMOTE variants such as polynomial SMOTE [25]…”
SMOTE for high-dimensional class-imbalanced data (2013, methodology): “For example, SMOTE was used for detecting network intrusions [10] or sentence boundary in speech [11], for predicting the distribution of species [12] or for detecting breast cancer [13].”
Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks (2021, background): “3047335 network intrusion detection [4], medical diagnosis [5], [6], electricity pilferage [7], and fraudulent transactions [8], among others.”