Leveraging Ensemble Learning Techniques for Enhanced Cybersecurity Threat Detection

Abstract

The rapid evolution of cyber threats has forced the development of modern intrusion detection systems (IDS) capable of identifying and combating sophisticated attacks. Traditional IDS techniques often fail to adapt to changing threats, resulting in high false-positive rates and insufficient accuracy. This study presents a robust intrusion detection framework employing ensemble learning techniques, specifically stacking, to boost cybersecurity threat detection. The research leverages the UNSW-NB15 dataset, a baseline for testing IDS, comprising multiple attack types and normal network traffic. The stacking ensemble combines Random Forest, Gradient Boosting, and XGBoost as foundation models with Logistic Regression as a meta-learner, providing a model that capitalizes on the complimentary qualities of its components. Rigorous preprocessing and feature engineering techniques are utilized to refine the dataset and increase model performance. Evaluation criteria, including accuracy, precision, recall, F1-Score, indicate the superiority of the suggested model. The stacking ensemble obtains an accuracy of 96.7%, a precision of 95.8%, and an F1-Score of 95.6%, greatly surpassing single models. The False Positive Rate is decreased to 2.1%, illustrating the model’s practical effectiveness in lowering false alarms and assuring reliable threat detection. This research emphasizes the potential of ensemble learning in boosting the adaptability, scalability, and resilience of IDS, addressing major concerns in modern cybersecurity. The findings provide a platform for establishing sophisticated, real-time detection systems and pave the way for future breakthroughs in intrusion detection approaches.