Design and analysis of genetic fuzzy systems for intrusion detection in computer networks

被引：39

作者：

Abadeh, Mohammad Saniee ^{[1
]}

Mohamadi, Hamid ^{[1
]}

Habibi, Jafar ^{[1
]}

机构：

[1] Sharif Univ Technol, Dept Comp Engn, Tehran, Iran

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 06期

关键词：

Genetic algorithms; Pattern recognition; Learning; Combinatorial problems; Fuzzy rule extraction; Intrusion detection; RULES; SELECTION;

D O I：

10.1016/j.eswa.2010.12.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The capability of fuzzy systems to solve different kinds of problems has been demonstrated in several previous investigations. Genetic fuzzy systems (GFSs) hybridize the approximate reasoning method of fuzzy systems with the learning capability of evolutionary algorithms. The objective of this paper is to design and analysis of various kinds of genetic fuzzy systems to deal with intrusion detection problem as a new real-world application area which is not previously tackled with GFSs. The resulted intrusion detection system would be capable of detecting normal and abnormal behaviors in computer networks. We have presented three kinds of genetic fuzzy systems based on Michigan, Pittsburgh and iterative rule learning (IRL) approaches to deal with intrusion detection as a high-dimensional classification problem. Experiments were performed with DARPA data sets which have information on computer networks, during normal and intrusive behaviors. The paper presents some results and compares the performance of different generated fuzzy rule sets in detecting intrusion in a computer network according to three different types of genetic fuzzy systems. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：7067 / 7075

页数：9

共 53 条

[1] Intrusion detection using a fuzzy genetics-based learning algorithm [J].