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Found 2 papers, 1 papers with code
SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
Large language models (LLMs) are versatile and can address many tasks, but for computational efficiency, it is often desirable to distill their capabilities into smaller student models.
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives
Applying the SMOTE oversampling technique and random undersampling, we create a balanced version of NSL-KDD and prove that skewed target classes in KDD-99 and NSL-KDD hamper the efficacy of classifiers on minority classes (U2R and R2L), leading to possible security risks.
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