# Testing the performance of Multi-class IDS public dataset using Supervised Machine Learning Algorithms

arXiv: https://arxiv.org/abs/2302.14374 · HTML: https://mlsanity.com/paper/2302.14374/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2023.

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

- [Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset](https://mlsanity.com/paper/2502.12382/) (2025)

## Abstract (arXiv)

Machine learning, statistical-based, and knowledge-based methods are often used to implement an Anomaly-based Intrusion Detection System which is software that helps in detecting malicious and undesired activities in the network primarily through the Internet. Machine learning comprises Supervised, Semi-Supervised, and Unsupervised Learning algorithms. Supervised machine learning uses a trained label dataset. This paper uses four supervised learning algorithms Random Forest, XGBoost, K-Nearest Neighbours, and Artificial Neural Network to test the performance of the public dataset. Based on the prediction accuracy rate, the results show that Random Forest performs better on multi-class Intrusion Detection System, followed by XGBoost, K-Nearest Neighbours respective, provided prediction accuracy is taken into perspective. Otherwise, K-Nearest Neighbours was the best performer considering the time of training as the metric. It concludes that Random Forest is the best-supervised machine learning for Intrusion Detection System