Ensuring network security with a robust intrusion detection system using ensemble-based machine learning

Abstract

Intrusion detection is a critical aspect of network security to protect computer systems from unauthorized access and attacks. The capacity of traditional intrusion detection systems (IDS) to identify unknown sophisticated threats is constrained by their reliance on signature-based detection. Approaches based on machine learning have shown promising results in identifying unknown malicious attacks. No learning algorithm-based model, however, is able to accurately and consistently detect all different kinds of attacks. Besides that, the existing models are tested for a specific dataset. In this research, a novel ensemble-based machine-learning technique for intrusion detection is presented. Numerous public datasets and multiple ensemble strategies, including Random Forest, Gradient Boosting, Adaboost, Gradient XGBoost, Bagging, and Simple Stacking, will be employed to evaluate the performance of the proposed approach. The most relevant features for the detection of intrusion are selected using correlation analysis, mutual information, and principal component analysis. Our research using different ensemble methods demonstrates that the proposed approach using the Random Forest technique outperforms existing approaches in terms of accuracy and FPR, typically exceeding 99% with better evaluation metrics like Precision, Recall, F1-score, Balanced Accuracy, Cohen's Kappa, etc. This strategy may be a useful tool for strengthening the safety of computer systems and networks against emerging cyber threats.