# Evaluation of Machine Learning Algorithms for Intrusion Detection System

arXiv: https://arxiv.org/abs/1801.02330 · HTML: https://mlsanity.com/paper/1801.02330/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2018.

## Built on by (most cited first, 9 of 9)

- [The Role of Machine Learning in Cybersecurity](https://mlsanity.com/paper/2206.09707/) (2022)
- [XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions](https://mlsanity.com/paper/2206.03585/) (2022)
- [An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks](https://mlsanity.com/paper/2101.12270/) (2021)
- [Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss](https://mlsanity.com/paper/2103.09713/) (2021)
- [Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks](https://mlsanity.com/paper/2212.14109/) (2022)
- [Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS](https://mlsanity.com/paper/2405.13066/) (2024)
- [A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks](https://mlsanity.com/paper/2502.06031/) (2025)
- [A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges](https://mlsanity.com/paper/2506.02438/) (2025)
- [Machine Learning Epidemic Predictions Using Agent-based Wireless Sensor Network Models](https://mlsanity.com/paper/2511.15982/) (2025)

## Abstract (arXiv)

Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate.