Title :
A study of intrusion detection system based on data mining
Author :
Miao, Chunyu ; Chen, Wei
Author_Institution :
Coll. of Xingzhi, Zhejiang Normal Univ., Jinhua, China
Abstract :
In this paper, classifications of intrusion detection and methods of data mining applied on them were introduced. Then, intrusion detection system design and implementation of based on data mining were presented. Such a system used APRIORI algorithm to analyse data association, which is the most influencing algorithm in mining Boolean association rules continuity item muster, with recurrence arithmetic based on idea of two period continuity item muster as core. Experiments showed that new type of attack can be detected effectively in the system, and knowledge base can be updated automatically, so the efficiency and accuracy of the intrusion detection were improved, and security of the network was enhanced.
Keywords :
Boolean functions; data mining; pattern classification; security of data; APRIORI algorithm; Boolean association rules mining; continuity item muster; data association; data mining; intrusion detection classification; intrusion detection system; network security; Analytical models; Correlation; Data mining; Data models; IP networks; Intrusion detection; data mining; intrusion detection; network security;
Conference_Titel :
Information Theory and Information Security (ICITIS), 2010 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6942-0
DOI :
10.1109/ICITIS.2010.5688763