REVOLUTIONIZING IDS A NOVEL FRAMEWORK USING JAYA OPTIMIZER AND SMOTE-ENN FOR CYBERATTACKS DETECTION

Network Intrusion Detection Systems (NIDS) are pivotal in addressing the increasing cybersecurity
threats by protecting computer networks from various types of cyberattacks. However, class
imbalances in datasets often reduce the effectiveness of detection algorithms, leading to
misclassification of minority classes. Additionally, poor feature selection can hinder performance and
increase computational load. In this study, we utilize the NSL-KDD and UNSW-NB15 datasets to
develop a robust IDS model. We applied Jaya Optimization for feature selection to enhance relevant
data features and utilized the SMOTE-ENN method for oversampling to balance the dataset. Various
algorithms were tested, including Decision Tree, Random Forest, Bagging with Decision Trees, J48,
ExtraTree, and a Voting Classifier combining ExtraTree with Boosted Decision Tree.