# Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier

arXiv: https://arxiv.org/abs/1904.01352 · HTML: https://mlsanity.com/paper/1904.01352/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RAN](https://mlsanity.com/paper/2105.01478/) (2021)
- [IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset](https://mlsanity.com/paper/2203.16365/) (2022)
- [Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis](https://mlsanity.com/paper/2401.03530/) (2024)
- [On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection](https://mlsanity.com/paper/2402.10974/) (2024)
- [Efficient Network Traffic Feature Sets for IoT Intrusion Detection](https://mlsanity.com/paper/2406.08042/) (2024)
- [A Novel Bio-Inspired Hybrid Multi-Filter Wrapper Gene Selection Method with Ensemble Classifier for Microarray Data](https://mlsanity.com/paper/2101.00819/) (2021)
- [Robust Attack Detection Approach for IIoT Using Ensemble Classifier](https://mlsanity.com/paper/2102.01515/) (2021)
- [Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network](https://mlsanity.com/paper/2207.07903/) (2022)
- [CAPoW: Context-Aware AI-Assisted Proof of Work based DDoS Defense](https://mlsanity.com/paper/2301.11767/) (2023)
- [Deep Neural Networks based Meta-Learning for Network Intrusion Detection](https://mlsanity.com/paper/2302.09394/) (2023)
- [Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection](https://mlsanity.com/paper/2210.02678/) (2022)
- [Rare Event Detection in Imbalanced Multi-Class Datasets Using an Optimal MIP-Based Ensemble Weighting Approach](https://mlsanity.com/paper/2412.13439/) (2024)
- [A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges](https://mlsanity.com/paper/2506.02438/) (2025)
- [SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2512.00251/) (2025)

## Abstract (arXiv)

Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics.