| Title | Network Intrusion Detection Based on Subspace Clustering and BP Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Chen, Shuyu, Li, Wei, Liu, Jun, Jin, Haoyu, Yin, Xuehui |
| Conference Name | 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom) |
| Keywords | BP Neural Network, cloud computing, Clustering algorithms, composability, Computational modeling, Conferences, Image edge detection, Intrusion detection, Metrics, network intrusion detection, Neural Network Security, Neural networks, new network attack, pubcrawl, resilience, Resiliency, subspace clustering |
| Abstract | 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. |
| DOI | 10.1109/CSCloud-EdgeCom52276.2021.00022 |
| Citation Key | chen_network_2021 |
Network Intrusion Detection Based on Subspace Clustering and BP Neural Network