Decision-Making Framework for Intrusion Detection in Networks Using XGBoost-Based Feature Selection and Deep Neural Networks

Abstract

The growing complexity and frequency of cyberattacks have rendered intrusion detection a vital aspect of network security. The conventional Intrusion Detection Systems (IDS) tend to be challenged with high-dimensional data and poor capability to detect variegated patterns of attacks. To address such issues, this study introduces a decision-making framework for intrusion detection through the integration of XGBoost-based feature selection and Deep Neural Network (DNN) classification. The suggested methodology was tested on two popular benchmark datasets: NSL-KDD and CIC-IDS 2017. Feature selection was first carried out by using the XGBoost algorithm to eliminate redundant and less informative features while preserving the most relevant attributes. This step enhanced the efficiency and accuracy of the model. The features were then classified using a DNN, which took advantage of its robust representation learning ability in identifying different types of attacks. Experimental results confirm the efficacy of the presented XGBoost–DNN approach. On the NSL-KDD dataset, the model performed with an accuracy of 99.69%, successfully identifying major attack categories such as. Likewise, on the CIC-IDS 2017 dataset, the model performed at 99.25% accuracy, showing excellent accuracy and recall for several contemporary attack types. These comparison of results with previous work demonstrate the strengths of the proposed methodology in dealing with heterogeneous and complex network traffic data.