# IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset

arXiv: https://arxiv.org/abs/2203.16365 · HTML: https://mlsanity.com/paper/2203.16365/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Feature Selection: A Data Perspective](https://mlsanity.com/paper/1601.07996/) (2016)
- [Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier](https://mlsanity.com/paper/1904.01352/) (2019)
- [Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection](https://mlsanity.com/paper/2111.13978/) (2021)
- [A Few-Shot Meta-Learning based Siamese Neural Network using Entropy Features for Ransomware Classification](https://mlsanity.com/paper/2112.00668/) (2021)

## Built on by (most cited first, 7 of 7)

- [Binary Feature Mask Optimization for Feature Selection](https://mlsanity.com/paper/2401.12644/) (2024)
- [Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models](https://mlsanity.com/paper/2502.11470/) (2025)
- [Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence](https://mlsanity.com/paper/2506.01869/) (2025)
- [Teacher-free Latent Self-distillation and Class-separable Representations for Lightweight IoT Attack Detection](https://mlsanity.com/paper/2403.15509/) (2024)
- [Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2408.15886/) (2024)
- [Iterative Feature Exclusion Ranking for Deep Tabular Learning](https://mlsanity.com/paper/2412.16442/) (2024)
- [AutoDP-LLM: Automating Data Pre-processing for Intrusion Detection Systems using Large Language Models](https://mlsanity.com/paper/2610.05369/) (2026)

## Abstract (arXiv)

The effectiveness of machine learning models is significantly affected by the size of the dataset and the quality of features as redundant and irrelevant features can radically degrade the performance. This paper proposes IGRF-RFE: a hybrid feature selection method tasked for multi-class network anomalies using a Multilayer perceptron (MLP) network. IGRF-RFE can be considered as a feature reduction technique based on both the filter feature selection method and the wrapper feature selection method. In our proposed method, we use the filter feature selection method, which is the combination of Information Gain and Random Forest Importance, to reduce the feature subset search space. Then, we apply recursive feature elimination(RFE) as a wrapper feature selection method to further eliminate redundant features recursively on the reduced feature subsets. Our experimental results obtained based on the UNSW-NB15 dataset confirm that our proposed method can improve the accuracy of anomaly detection while reducing the feature dimension. The results show that the feature dimension is reduced from 42 to 23 while the multi-classification accuracy of MLP is improved from 82.25% to 84.24%.