# Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications

arXiv: https://arxiv.org/abs/2504.18771 · HTML: https://mlsanity.com/paper/2504.18771/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Self-paced Ensemble for Highly Imbalanced Massive Data Classification](https://mlsanity.com/paper/1909.03500/) (2019)
- [Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost](https://mlsanity.com/paper/1908.01672/) (2019)
- [Tree Boosting Methods for Balanced andImbalanced Classification and their Robustness Over Time in Risk Assessment](https://mlsanity.com/paper/2504.18133/) (2025)

## Abstract (arXiv)

This work empirically evaluates machine learning models on two imbalanced public datasets (KDDCUP99 and Credit Card Fraud 2013). The method includes data preparation, model training, and evaluation, using an 80/20 (train/test) split. Models tested include eXtreme Gradient Boosting (XGB), Multi Layer Perceptron (MLP), Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Multiple-Objective Generative Adversarial Active Learning (MO-GAAL), with XGB and MLP further combined with Random-Over-Sampling (ROS) and Self-Paced-Ensemble (SPE). Evaluation involves 5-fold cross-validation and imputation techniques (mean, median, and IterativeImputer) with 10, 20, 30, and 50 % missing data. Findings show XGB and MLP outperform generative models. IterativeImputer results are comparable to mean and median, but not recommended for large datasets due to increased complexity and execution time. The code used is publicly available on GitHub (github.com/markushaug/acr-25).