Title :
A Mixed Unsupervised Clustering-Based Intrusion Detection Model
Author :
Zhang, Cuixiao ; Zhang, Guobing ; Sun, Shanshan
Author_Institution :
Sch. of Comput. & Inf., Shijiazhuang Railway Inst., Shijiazhuang, China
Abstract :
Through analyzing the advantages and disadvantages between anomaly detection and misuse detection, a mixed intrusion detection system (IDS) model is designed. First, data is examined by the misuse detection module, then abnormal data detection is examined by anomaly detection module. In this model, the anomaly detection module is built using unsupervised clustering method, and the algorithm is an improved algorithm of K-means clustering algorithm and it is proved to have high detection rate in the anomaly detection module.
Keywords :
pattern clustering; security of data; unsupervised learning; abnormal data detection; anomaly detection module; detection rate; intrusion detection system model; k-means clustering algorithm; misuse detection module; mixed unsupervised clustering-based intrusion detection model; Clustering algorithms; Clustering methods; Computer crime; Computer networks; Data security; Genetics; Information analysis; Information security; Intrusion detection; Sun; anomaly detection; clustering algorithm; intrusion detection model; unsupervised cluster;
Conference_Titel :
Genetic and Evolutionary Computing, 2009. WGEC '09. 3rd International Conference on
Conference_Location :
Guilin
Print_ISBN :
978-0-7695-3899-0
DOI :
10.1109/WGEC.2009.72
