Hybrid Sequential Feature Selection With Ensemble Boosting Class-based Classification Method

Abstract: The rapid rise in hacking and computer network assaults throughout the world has highlighted the need for more effective intrusion detection and prevention solutions. The intrusion detection system (IDS) is critical in identifying abnormalities and assaults on the network, which have grown in size and scope. IDS prevents intruders from gaining access to information in the field of network security as a result. The use of IDS is critical for detecting various types of attacks. Because the network traffic dataset contains a large number of features, the process of selecting and removing irrelevant features improves the accuracy of the classification algorithms. For the fact that a large dimension allows us to include more data, the feature vector can be built by combining different types of features. Contains a lot of redundant or irrelevant data can cause confusion. Over-fitting issues and a decrease in the generalization capacity of the model. Solving such a problem necessitates a sequence of feature selection methods the boosted maximum relevance maximum distance (BMRMD) method can report on the contribution of each feature as well as the predictive accuracy based on the best feature set. As a result, the best features in this study were chosen using the BMRMD assesses feature redundancy to determine feature relevance to the target class based on optimum ensemble feature classification