# Efficient Network Traffic Feature Sets for IoT Intrusion Detection

arXiv: https://arxiv.org/abs/2406.08042 · HTML: https://mlsanity.com/paper/2406.08042/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier](https://mlsanity.com/paper/1904.01352/) (2019)
- [Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection](https://mlsanity.com/paper/2203.04234/) (2022)
- [A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection](https://mlsanity.com/paper/2111.13149/) (2021)
- [Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification](https://mlsanity.com/paper/2301.13122/) (2023)
- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [Constrained Adversarial Learning for Automated Software Testing: a literature review](https://mlsanity.com/paper/2303.07546/) (2023)
- [An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection](https://mlsanity.com/paper/2402.16912/) (2024)
- [Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection](https://mlsanity.com/paper/2404.04188/) (2024)

## Built on by (most cited first, 2 of 2)

- [Intelligent Green Efficiency for Intrusion Detection](https://mlsanity.com/paper/2411.08069/) (2024)
- [Revisiting Network Traffic Analysis: Compatible network flows for ML models](https://mlsanity.com/paper/2511.08345/) (2025)

## Abstract (arXiv)

The use of Machine Learning (ML) models in cybersecurity solutions requires high-quality data that is stripped of redundant, missing, and noisy information. By selecting the most relevant features, data integrity and model efficiency can be significantly improved. This work evaluates the feature sets provided by a combination of different feature selection methods, namely Information Gain, Chi-Squared Test, Recursive Feature Elimination, Mean Absolute Deviation, and Dispersion Ratio, in multiple IoT network datasets. The influence of the smaller feature sets on both the classification performance and the training time of ML models is compared, with the aim of increasing the computational efficiency of IoT intrusion detection. Overall, the most impactful features of each dataset were identified, and the ML models obtained higher computational efficiency while preserving a good generalization, showing little to no difference between the sets.