Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

The exponential growth of Internet and network usage has necessitated heightened security measures to protect against data and network breaches. Intrusions, executed through network packets, pose a significant challenge for firewalls to detect and prevent due to the similarity between legitimate and intrusion traffic. The vast network traffic volume also complicates most network monitoring systems and algorithms. Several intrusion detection methods have been proposed, with machine learning techniques regarded as promising for dealing with these incidents. This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base (Random Forest, Decision Tree, and k-Nearest-Neighbors). The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms. The stacking ensemble technique increases performance by incorporating three base models (Random Forest, Decision Tree, and k-Nearest-Neighbors) and a meta-model represented by the Logistic Regression algorithm. Evaluated using the UNSW-NB15 dataset, the proposed IDS gained an accuracy of 96.16% in the training phase and 97.95% in the testing phase, with precision of 97.78%, and 98.40% for taring and testing, respectively. The obtained results demonstrate improvements in other measurement criteria.