Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset - Yin, Yuhua; Jang-Jaccard, Julian; Sabrina, Fariza; Kwak, Jin (Proceedings of the 2023 26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023, 2023)
The network intrusion threats are increasingly severe with the application of computer supported coorperative work. 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 K-means 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.
Keywords: Anomaly detection; CICIDS2017 dataset; Clusterings; Intrusion Detection Systems; Intrusion-Detection; Machine learning algorithms; Multi-classification; Multilayers perceptrons; Network anomaly detection; Network intrusions; Anomaly Detection; CICIDS2017 dataset; Clustering Algorithm; Intrusion Detection; Multi-classification; Multilayer Perceptron