Research Article
Intrusion Detection System based on Learning Fuzzy Rules and Membership Functions using Genetic Algorithms
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 73 - Issue 13 |
| Published: July 2013 |
| Authors: Ezat Soleiman, Abdelhamid Fetanat |
10.5120/12805-0235
|
Ezat Soleiman, Abdelhamid Fetanat . Intrusion Detection System based on Learning Fuzzy Rules and Membership Functions using Genetic Algorithms. International Journal of Computer Applications. 73, 13 (July 2013), 44-47. DOI=10.5120/12805-0235
@article{ 10.5120/12805-0235,
author = { Ezat Soleiman,Abdelhamid Fetanat },
title = { Intrusion Detection System based on Learning Fuzzy Rules and Membership Functions using Genetic Algorithms },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 73 },
number = { 13 },
pages = { 44-47 },
doi = { 10.5120/12805-0235 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A Ezat Soleiman
%A Abdelhamid Fetanat
%T Intrusion Detection System based on Learning Fuzzy Rules and Membership Functions using Genetic Algorithms%T
%J International Journal of Computer Applications
%V 73
%N 13
%P 44-47
%R 10.5120/12805-0235
%I Foundation of Computer Science (FCS), NY, USA
Abstract
With the rapid expansion of Internet in recent years, computer systems are facing increased number of security threats. Despite numerous technological innovations for information assurance, it is still very difficult to protect computer systems. Therefore, unwanted intrusions take place when the actual software systems are running. Different soft computing based approaches have been proposed to detect computer network attacks. Hybrid methods proved more effective and accurate, this paper tries to introduce how to use dynamic fuzzy rules and genetic algorithm in intrusion detection systems.
References
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