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

ABSTRACT: 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.
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.
Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset
http://arxiv.org/abs/2208.09711v2