An Intelligent Intrusion Detection System Using Ensemble Learning for Ultra-Dense IoT Networks

Abstract

Intrusion detection refers to the process of observing and analyzing network or system incidents in a perpetual manner to identify unauthorized accesses, malicious acts, or violations of the rules. It plays a pivotal role in the protection of critical information, the prevention of security breaches, and the safety, confidentiality, and availability of company assets. Strong methods to identify and stop harmful activity are required because cybersecurity threats have grown more complex due to the quick expansion of digital infrastructure. Various researchers have conducted different research studies for intrusion detection, and different methodologies, along with traditional as well as machine learning models, have been applied with various datasets for the proposed task. This research aims to address these challenges by developing an efficient and intelligent intrusion detection system using a stacking ensemble learning approach. The proposed model integrates multiple base classifiers: Decision Tree, Naïve Bayes, K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA) to capture diverse decision boundaries, with a Random Forest acting as the meta-classifier to aggregate and optimize final predictions. The publicly available UNSW-NB15 dataset is employed in this study for intrusion detection. Python and its libraries are used for simulation purposes. After simulation, it has been achieved that the stacked model, which combines the predictions of multiple base learners through a meta-classifier, achieved a significantly higher accuracy of 99.93%. While in comparison, LDA achieved the highest accuracy of 94.25%, followed closely by SVM at 93.05%, DT at 91.00%, NB at 90.55%, and KNC at 89.81%. This demonstrates that ensemble learning, particularly stacking, can effectively leverage the strengths of individual models to greatly enhance intrusion detection performance for complex datasets