HCTDDA: Hybrid Classification Technique for Detection of DDoS Attacks

Today attackers are turning to mobile and Internet of Things (IoT) technologies to diversify and strengthen DDoS Attacks. Many methods exist for detecting different types of DDoS attacks. In this paper, the authors proposed HCTDDA (hybrid classification technique for detection of DDoS attacks) for detecting DDoS attacks by using machine learning and deep learning methods (Random Forest, Decision Tree, SNN, DNN and XGBoost) in combination with various feature selection methods. The goal of proposing HCTDDA is early and accurate detection of DDoS attacks. To achieve this goal, we apply the feature selection methods (Chi-Square, Extra Tree, ANOVA and Mutual Information) to determine the appropriate attributes that are better deliverables for the prediction model. After analyzing the results Mutual Information Feature selection at 45 features gives 96.77
^%
as the highest accuracy with a feature reduction rate of 43.04% for early detection of DDoS attacks.