# Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration

arXiv: https://arxiv.org/abs/2112.02080 · HTML: https://mlsanity.com/paper/2112.02080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [A Survey of Network-based Intrusion Detection Data Sets](https://mlsanity.com/paper/1903.02460/) (2019)
- [Towards a Standard Feature Set for Network Intrusion Detection System Datasets](https://mlsanity.com/paper/2101.11315/) (2021)
- [Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives](https://mlsanity.com/paper/1811.05372/) (2018)
- [Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection](https://mlsanity.com/paper/2008.03297/) (2020)
- [Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle](https://mlsanity.com/paper/1906.11976/) (2019)
- [Shift Happens: Adjusting Classifiers](https://mlsanity.com/paper/2111.02529/) (2021)
- [MSNM-Sensor: An Applied Network Monitoring Tool for Anomaly Detection in Complex Networks and Systems](https://mlsanity.com/paper/1907.13612/) (2019)

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

- [Enhancing web traffic attacks identification through ensemble methods and feature selection](https://mlsanity.com/paper/2412.16791/) (2024)

## Abstract (arXiv)

This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML models to work on integrated datasets, empowering the learning process with diverse information from different datasets. Therefore, R-NIDS targets the design of more robust models, that generalize better than traditional approaches. We also propose a new dataset, called UNK21. It is built from three of the most well-known network datasets (UGR'16, USNW-NB15 and NLS-KDD), each one gathered from its own network environment, with different features and classes, by using a data aggregation approach present in R-NIDS. Following R-NIDS, in this work we propose to build two well-known ML models (a linear and a non-linear one) based on the information of three of the most common datasets in the literature for NIDS evaluation, those integrated in UNK21. The results that the proposed methodology offers show how these two ML models trained as a NIDS solution could benefit from this approach, being able to generalize better when training on the newly proposed UNK21 dataset. Furthermore, these results are carefully analyzed with statistical tools that provide high confidence on our conclusions.