# Bridging the gap to real-world for network intrusion detection systems with data-centric approach

arXiv: https://arxiv.org/abs/2110.13655 · HTML: https://mlsanity.com/paper/2110.13655/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 1 of 1)

- [A Survey of Network-based Intrusion Detection Data Sets](https://mlsanity.com/paper/1903.02460/) (2019)

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

- [The Role of Machine Learning in Cybersecurity](https://mlsanity.com/paper/2206.09707/) (2022)
- [An Evaluation Framework for Network IDS/IPS Datasets: Leveraging MITRE ATT&CK and Industry Relevance Metrics](https://mlsanity.com/paper/2511.12743/) (2025)

## Abstract (arXiv)

Most research using machine learning (ML) for network intrusion detection systems (NIDS) uses well-established datasets such as KDD-CUP99, NSL-KDD, UNSW-NB15, and CICIDS-2017. In this context, the possibilities of machine learning techniques are explored, aiming for metrics improvements compared to the published baselines (model-centric approach). However, those datasets present some limitations as aging that make it unfeasible to transpose those ML-based solutions to real-world applications. This paper presents a systematic data-centric approach to address the current limitations of NIDS research, specifically the datasets. This approach generates NIDS datasets composed of the most recent network traffic and attacks, with the labeling process integrated by design.