Title :
A Parallel Clustering Ensemble Algorithm for Intrusion Detection System
Author :
Gao, Hongwei ; Zhu, Dingju ; Wang, Xiaomin
Author_Institution :
Cloud Comput. Lab., Chinese Acad. of Sci., Shenzhen, China
Abstract :
Clustering analysis is a common unsupervised anomaly detection method, and often used in Intrusion Detection System (IDS), which is an important component in the network security. The single cluster algorithm is difficult to get the great effective detection, and then a new cluster algorithm based on evidence accumulation is born. The IDS with clustering ensemble has a low false positive rate and high detection rate, however, the IDS is slow to detect the mass data stream, and it can not detect the attacks in time. This paper presents a parallel clustering ensemble algorithm to improve the speed and the effective of the system. Finally, the KDDCUP99 data set is used to test the system show that the IDS have greatly improvement in time and efficiency.
Keywords :
computer network security; pattern clustering; KDDCUP99 data set; clustering analysis; evidence accumulation; intrusion detection system; network security; parallel clustering ensemble algorithm; unsupervised anomaly detection method; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Intrusion detection; Partitioning algorithms; Program processors; Strontium; Evidence Accumulation; Intrusion Detection System; Parallel Clustering Ensemble;
Conference_Titel :
Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7539-1
DOI :
10.1109/DCABES.2010.98
