Research on Network Attack Detection Approach Based on Multi-clustering

This paper presents a new multi-clustering based network attack detection model (MBNADM). Firstly, the improved spatial clustering (ISC) algorithm was designed to clustering subspace. Secondly, the improved density based clustering (IDBC) algorithm was used to complete the fine-grained aggregation operation on the spatial data sets and the k value setting. Finally, the distinct distance values among each isolated point (network attack) and clustered centroid were calculated with the improved evidence accumulation clustering (IEAC) algorithm, then the matrix clustering algorithm was used to calculate the detection threshold and consequently the network attacks in network behaviors were determined. The attack detection experiments on KDD99 dataset and the detection comparison experiment of different detection methods demonstrate that the MBNADM method has a high detection rate and low false positive rate.