SGB-IDS: A SWARM GRADIENT BOOSTING INTRUSION DETECTION SYSTEM USING HYBRID FEATURE SELECTION FOR ENHANCED NETWORK SECURITY

This paper proposes an integrated approach to build up IDS with proper effectiveness toward the rising need for strong network security. Network traffic anomaly detection and classification are one of the major aims and enhance the security layer against various types of cyber threats. This study is a methodical approach in which a diverse set of data is first extracted from Kaggle. The collected dataset is a comprehensive one that includes various kinds of network traffic data. The first step includes preprocessing the data, i.e., handling missing values, removing erroneous entries, and dealing with outliers using the Z-score method. To counter class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is utilized in generating synthetic samples in underrepresented classes for the generalization of models. The feature selection is done by using the Variance Mutual Forest (VMF) algorithm relies on the Variance Thresholding, Mutual Information, and Random Forest Selection methods. This method unites Variance Thresholding, to select statistically significant features with Mutual Information, and Random Forest for feature dimension reduction with the goal of overfitting minimization. For building models, a hybrid of Particle Swarm Optimization (PSO) and Light Gradient Boosting Machine (LightGBM), which is termed Swarm Gradient Boosting (SGB), is used. By using soft voting to aggregate the outputs of PSO and LightGBM, the proposed SGB model improves the prediction accuracy, the degree of robustness, and adaptability. The presented methodology has achieved high classification accuracy of 97.28%, precision of 93.49%, recall of 91.88%, F1 score of 94.23%, and low RMSE of 0.2592. These metrics demonstrate that the model is reliable and of practical use for intrusion detection in dynamic, high-dimensional environments of networks, providing a proper solution to modern security network challenges.