# Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

arXiv: https://arxiv.org/abs/2204.00144 · HTML: https://mlsanity.com/paper/2204.00144/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 73 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification](https://mlsanity.com/paper/1805.06546/) (2018)
- [LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices](https://mlsanity.com/paper/1812.04818/) (2018)
- [An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks](https://mlsanity.com/paper/2101.12270/) (2021)

## Built on by (most cited first, 11 of 11)

- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data](https://mlsanity.com/paper/2310.16524/) (2023)
- [Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data](https://mlsanity.com/paper/2304.03722/) (2023)
- [Synthetic data, real errors: how (not) to publish and use synthetic data](https://mlsanity.com/paper/2305.09235/) (2023)
- [Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark](https://mlsanity.com/paper/2310.16981/) (2023)
- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)
- [Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production](https://mlsanity.com/paper/2311.09333/) (2023)
- [Outlier Detection in Plantar Pressure: Human-Centered Comparison of Statistical Parametric Mapping and Explainable Machine Learning](https://mlsanity.com/paper/2509.21943/) (2025)
- [Population synthesis with geographic coordinates](https://mlsanity.com/paper/2510.09669/) (2025)
- [Using Synthetic Data to estimate the True Error is theoretically and practically doable](https://mlsanity.com/paper/2511.00964/) (2025)
- [Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation](https://mlsanity.com/paper/2602.03791/) (2026)

## Abstract (arXiv)

Attacks on computer networks have increased significantly in recent days, due in part to the availability of sophisticated tools for launching such attacks as well as thriving underground cyber-crime economy to support it. Over the past several years, researchers in academia and industry used machine learning (ML) techniques to design and implement Intrusion Detection Systems (IDSes) for computer networks. Many of these researchers used datasets collected by various organizations to train ML models for predicting intrusions. In many of the datasets used in such systems, data are imbalanced (i.e., not all classes have equal amount of samples). With unbalanced data, the predictive models developed using ML algorithms may produce unsatisfactory classifiers which would affect accuracy in predicting intrusions. Traditionally, researchers used over-sampling and under-sampling for balancing data in datasets to overcome this problem. In this work, in addition to over-sampling, we also use a synthetic data generation method, called Conditional Generative Adversarial Network (CTGAN), to balance data and study their effect on various ML classifiers. To the best of our knowledge, no one else has used CTGAN to generate synthetic samples to balance intrusion detection datasets. Based on extensive experiments using a widely used dataset NSL-KDD, we found that training ML models on dataset balanced with synthetic samples generated by CTGAN increased prediction accuracy by up to 8%, compared to training the same ML models over unbalanced data. Our experiments also show that the accuracy of some ML models trained over data balanced with random over-sampling decline compared to the same ML models trained over unbalanced data.