Network Intrusion Detection Based on Subspace Clustering and BP Neural Network

摘要

This paper proposes a novel network intrusion detection algorithm based on the combination of Subspace Clustering (SSC) and BP neural network. Firstly, we perform a subspace clustering algorithm on the network data set to obtain different subspaces. Secondly, BP neural network intrusion detection is carried out on the data in different subspaces, and calculate the prediction error value. By comparing with the pre-set accuracy, the threshold is constantly updated to improve the ability to identify network attacks. By comparing with K-means, DBSCAN, SSC-EA and k-KNN intrusion detection model, the SSC-BP neural network model can detect the most attacked networks with the lowest false detection rate.