FSMDAD: Feature Selection Method for DDoS Attack Detection
Vimal Gaur, Rajneesh Kumar · 2022 International Conference on Electronics and Renewable Systems (ICEARS) · 2022
Feature Selection is selecting relevant features according to the feature scores. It is the most traditional process of eliminating irrelevant features, reducing dimensionality, and improving classification accuracy. This paper proposes a FSMDAD model for selecting the top ten features using Chi-Square, Extra Tree, ANOVA and Mutual Information feature selection methods. Further, most influencing features have been derived as: total time between two packets in the forward direction (FwdIATTotal) and duration of the flow in microseconds (FlowDuration). Finally, a series of iterations have been performed by integrating the above feature selection methods with machine learning classifiers (random forest and decision tree). Random forest and Decision Tree give dominant results with the extra tree method. Since Extra Tree classifier performs best, so best features obtained for different types of DDoS attacks have been calculated using this classifier.