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

Authors: Yuhua Yin, Julian Jang-Jaccard, Fariza Sabrina, Jin Kwak
Published: 2022-08-20 | Updated: 2022-10-30

Source: https://arxiv.org/abs/2208.09711

PDF: https://arxiv.org/pdf/2208.09711

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機械学習技術 攻撃検出 トレーニングプロトコル

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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 using Birch clustering, which is better than similar researches using a stand-alone MLP model.