What is the role of feature selection in improving the performance and efficiency of machine learning models for DDoS detection on CIC-DDoS2019?

Feature selection reduces dimensionality, removes irrelevant or redundant features, and improves model interpretability. Techniques include: 1) Filter methods (e.g., information gain, chi-squared test); 2) Wrapper methods (e.g., recursive feature elimination); 3) Embedded methods (e.g., L1 regularization). Selecting the most informative features can lead to faster training times, reduced overfitting, and improved detection accuracy.