文章基本信息

标题：Intelligent Network Intrusion Detection Using DT and BN Classification Techniques
本地全文：下载
作者：P. Srinivasulu ; R. Satya Prasad ; I. Ramesh Babu 等
期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2010
卷号：2
期号：1
出版社：International Center for Scientific Research and Studies
摘要：Security is becoming a critical part of organizational information systems. Network Intrusion Detection System (NIDS) is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. The detection of attacks against computer networks is becoming a harder problem to solve in the field of Network security. Intrusion Detection is an essential mechanism to protect computer systems from many attacks. The success of an intrusion detection system depends on the selection of the appropriate features in detecting the intrusion activity. In NIDS electing unnecessary features may cause computational issues and decrease the accuracy of detection. This paper describes a technique of applying Genetic Algorithm (GA) to choose features (attributes) of KDDCUP99 Dataset. We have chosen the standard dataset KDDCUP from MIT, U.S.A, which is used for IDS research oriented projects. In this paper a brief overview of the Intrusion Detection System and genetic algorithm is presented. We used Bayes Network (BN), and Decision Tree (DT) Tree approaches for classifying the network attacks for chosen attribute dataset. These models gave better performance compared with the all features of dataset
关键词：Genetic Algorithms; Classification methods; Confusion matrix; ;Fitness function; and Crossover