# Orthogonal variance-based feature selection for intrusion detection systems

arXiv: https://arxiv.org/abs/2110.12627 · HTML: https://mlsanity.com/paper/2110.12627/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 25 Oct 2021.

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

- [Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection](https://mlsanity.com/paper/2008.03297/) (2020)
- [Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments](https://mlsanity.com/paper/1812.05443/) (2018)
- [Gamma distribution-based sampling for imbalanced data](https://mlsanity.com/paper/2009.10343/) (2020)
- [Feature selection for intrusion detection systems](https://mlsanity.com/paper/2106.14941/) (2021)

## Built on by (most cited first, 1 of 1)

- [Orthogonal Activation with Implicit Group-Aware Bias Learning for Class Imbalance](https://mlsanity.com/paper/2512.20006/) (2025)

## Abstract (arXiv)

In this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method.