Investigating rarity in web attacks with ensemble learners

Abstract

Class rarity is a frequent challenge in cybersecurity. Rarity occurs when the positive (attack) class only has a small number of instances for machine learning classifiers to train upon, thus making it difficult for the classifiers to discriminate and learn from the positive class. To investigate rarity, we examine three individual web attacks in big data from the CSE-CIC-IDS2018 dataset: “Brute Force-Web”, “Brute Force-XSS”, and “SQL Injection”. These three individual web attacks are also severely imbalanced, and so we evaluate whether random undersampling (RUS) treatments can improve the classification performance for these three individual web attacks. The following eight different levels of RUS ratios are evaluated: no sampling, 999:1, 99:1, 95:5, 9:1, 3:1, 65:35, and 1:1. For measuring classification performance, Area Under the Receiver Operating Characteristic Curve (AUC) metrics are obtained for the following seven different classifiers: Random Forest (RF), CatBoost (CB), LightGBM (LGB), XGBoost (XGB), Decision Tree (DT), Naive Bayes (NB), and Logistic Regression (LR) (with the first four learners being ensemble learners and for comparison, the last three being single learners). We find that applying random undersampling does improve overall classification performance with the AUC metric in a statistically significant manner. Ensemble learners achieve the top AUC scores after massive undersampling is applied, but the ensemble learners break down and have poor performance (worse than NB and DT) when no sampling is applied to our unique and harsh experimental conditions of severe class imbalance and rarity.