Title :
An intrusion detection method based on decision tree
Author :
Liu, Yongjin ; Li, Na ; Shi, Leina ; Li, Fangping
Author_Institution :
Sch. of Inf. Eng., Handan Coll., Handan, China
Abstract :
How to find the intrusion behaviors is a problem that troubled the intrusion detection field for years. Until now, there is not a good method to solve it, epically in a realistic context. Most methods are effective on small data sets, but when used to the massive data of IDS, the effectiveness seems to be unsatisfactory. In this paper, a new method based on decision tree is discussed to solve the problem of low detection rate of massive data.
Keywords :
decision trees; security of data; decision tree; detection rate; intrusion behavior; intrusion detection method; massive data; Bagging; Boosting; Classification tree analysis; Decision trees; Ecosystems; Information systems; Intrusion detection; Large-scale systems; Stability; agent; decision tree; intrusion detection; random decision tree;
Conference_Titel :
E-Health Networking, Digital Ecosystems and Technologies (EDT), 2010 International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-5514-0
DOI :
10.1109/EDT.2010.5496597
