Optimized Web Server Attack Detection: A Super Learner Ensemble Model Approach

Abstract

Web applications are essential for many organizations, yet they are vulnerable to various security threats, such as injection attacks and inadequate authentication mechanisms. To address these risks, this study proposes a super learner ensemble learning model that combines multiple machine learning (ML) algorithms to improve web server attack detection. Leveraging the unique strengths of each base ML model, the super learner approach enhances predictive accuracy by using a meta-model trained on out-of-fold predictions from base learners, achieving superior performance in identifying attacks. The proposed model was evaluated on the UNSW-NB 15 and KDD CUP 99 datasets, achieving impressive detection accuracies of 99.69% and 99.90%, respectively. This ensemble model effectively addresses challenges in cybersecurity, such as high false-positive rates and imbalanced data, by employing adaptive synthetic sampling and feature selection. Comparative analysis reveals that the super learner model outperforms existing detection methods, improving detection accuracy by up to 9.54%. These findings suggest that the super learner ensemble approach is a promising method for enhancing the security of web applications. Future work could expand on these results by exploring different base models, datasets, and real-time anomaly detection mechanisms to further improve web server protection.