Distributed intrusion detection based on clustering

Abstract

The research on distributed intrusion detection system (DIDS) is a rapidly growing area of interest because the existence of centralized intrusion detection system (IDS) techniques is increasingly unable to protect the global distributed information infrastructure. Distributed analysis employed by agent-based DIDS is an accepted fabulous method. Clustering-based intrusion detection technique overcomes the drawbacks of relying on labeled training data which most current anomaly-based intrusion detection depend on. Clustering-based DIDS technique according to the advantages of two techniques is presented. For effectively choosing the attacks, twice clustering is employed: the first clustering is to choose the candidate anomalies at agent IDS and the second clustering is to choose the true attack at central IDS. At last, through experiment on the KDD CUP 1999 data records of network connections verified that the methods put forward is better.