A new Proactive Feature Selection Model PFS Based on the Enhanced Optimization Algorithms to Detect DRDoS Attacks

Cyberattacks have grown steadily over the last few years. The Distributed Reflection Denial of Service (DRDoS) attack, a new variant of Distributed Denial of Service (DDoS) attack, has been rising. These types of attacks leave devastating effects on the target. DRDoS attacks are more difficult to mitigate due to the dynamics and the attack strategy of this type of attack. The number of features influences the performance of the intrusion detection system by investigating the behaviour of traffic. Therefore, the feature selection model improves the accuracy of the detection mechanism also reduces the time of detection by reducing the number of features. The proposed model aims to detect DRDoS attacks based on the feature selection model, and this model is called a proactive feature selection model PFS. This model uses a nature-inspired optimization algorithm for the feature subset selection. Three machine learning algorithms, i.e., KNN, RF, and SVM, were evaluated as the potential classifier for evaluating the selected features. We have used the CICDDoS2019 dataset for evaluation purposes, and the performance of each classifier is compared to previous models. The results indicate that the suggested model works better than the current approaches providing a higher detection rate, a low false-positive rate, and increased detection accuracy. The PFS model shows better accuracy to detect DRDoS attacks with a 90.81% detection rate.