Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives

1 code implementation • 13 Nov 2018 • Abhishek Divekar, Meet Parekh, Vaibhav Savla, Rudra Mishra, Mahesh Shirole

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.

Benchmarking F1 Score +1