Title :
A novel adaptive intrusion detection system based on data mining
Author :
Yu, Zhi-Xin ; Chen, Jing-Ran ; Zhu, Tian-Qing
Author_Institution :
Sch. of Electron. & Inf., Wuhan Univ., China
Abstract :
A data mining based adaptive intrusion detection model (DMAIDM) is presented in this paper. The DMAIDM applies a fast heuristic clustering algorithm for mixed data (FHCAM) to distinguish intrusions from legal behaviors efficiently and an attribute-constrained based fuzzy mining algorithm (ACFMA) to construct intrusion pattern-database automatically. Verification tests are carried out by using the 10% subset of KDD Cup 1999 data set, the average detection rate is 71.67% and the average false detection rate is 0.92%. And the detection rate increases from 65.25% (the second subset) to 85.7% (the ninth subset) adaptively. The experimental results reveal that the DMAIDM is successful in terms of not only accuracy but also efficiency in networks intrusion detection.
Keywords :
data mining; pattern clustering; security of data; KDD Cup 1999 data set; adaptive intrusion detection system; attribute-constrained based fuzzy mining algorithm; data mining; heuristic clustering algorithm; intrusion pattern-database; verification tests; Adaptive systems; Clustering algorithms; Data mining; Data security; Heuristic algorithms; Intrusion detection; Law; Legal factors; Pattern matching; Transaction databases; Data Mining; Fuzzy Mining; Intrusion Detection; Partition-based Clustering;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527344
