Title :
Cluster ensemble for intrusion detection systems
Author :
Zhou, Peng ; Li, Zhishu
Author_Institution :
Sch. of Comput. Sci., Sichuan Univ., Chengdu, China
Abstract :
The paper introduces cluster ensemble for intrusion detection systems. Intrusion detection is a hard problem which is studied by many researchers and is a research hot pot. The idea is that we use cluster ensemble to decide which net-event is normal or unnormal. In this paper there are three works presented. First, we state a hard cluster ensemble method. Second, the model of cluster ensemble for IDS is illustrated in detail. Third, some UCI datasets and KDD99 dataset are chosen for the experiments, and the results show that cluster ensemble for IDS is better than any single algorithm or model.
Keywords :
pattern clustering; security of data; IDS; KDD99 dataset; UCI datasets; cluster ensemble method; intrusion detection systems; Clustering algorithms; Computer science; Data mining; Data privacy; Distributed computing; Educational institutions; Intrusion detection; Machine learning; Protection; Security; Intrusion Detection Systems; cluster ensemble;
Conference_Titel :
Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5263-7
Electronic_ISBN :
978-1-4244-5265-1
DOI :
10.1109/ICIME.2010.5478129
