# Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset

arXiv: https://arxiv.org/abs/2208.09711 · HTML: https://mlsanity.com/paper/2208.09711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 20 Aug 2022.

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

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

## Abstract (arXiv)

Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP classifier to improve the performance of network anomaly multi-classification. In our proposed method, we first apply Birch or Kmeans as an unsupervised clustering algorithm to the CICIDS-2017 dataset to pre-group the data. The generated pseudo-label is then added as an additional feature to the training of the MLP-based classifier. The experimental results show that using Birch and K-Means clustering for data pre-grouping can improve intrusion detection system performance. Our method can achieve 99.73% accuracy in multi-classification using Birch clustering, which is better than similar researches using a stand-alone MLP model.