Research of Intrusion Detection Based on Clustering Analysis

On the basis of the research of existing intrusion detection technology, the paper establishes an intrusion detection model based on clustering analysis. It perfects the shortcomings existing in traditional one. Meanwhile, in order to improve the shortages of traditional clustering analysis algorithm k-means that it needs to know the number of clustering at the beginning and it is sensitive to initial clustering center, improved k-means algorithm is put forward. It chooses authority data set KDD Cup1999 in the intrusion detection field as experimental data to verify its performance. The experiments show that this algorithm has higher detection rate and lower false positive rate