Open AccessAn Intelligent Agent Based Intrusion Detection System Using Fuzzy Rough Set Based Outlier Detection
Author(s) -
Jaisankar Narayanasamy,
Sannasi Ganapathy,
P. Yogesh,
A. Kannan,
K. Anand
Publication year - 2011
Publication title -
international journal of electronic signal and systems
Language(s) - English
Resource type - Journals
ISSN - 2231-5969
DOI - 10.47893/ijess.2011.1018
Subject(s) - intrusion detection system , rough set , constant false alarm rate , anomaly detection , data mining , computer science , support vector machine , artificial intelligence , outlier , fuzzy logic , pattern recognition (psychology) , fuzzy set , feature selection , feature (linguistics) , set (abstract data type) , false positive rate , linguistics , philosophy , programming language
Since existing Intrusion Detection Systems (IDS) including misuse detection and anomoly detection are generally incapable of detecting new type of attacks. However, all these systems are capable of detecting intruders with high false alarm rate. It is an urgent need to develop IDS with very high Detection rate and with low False alarm rate. To satisfy this need we propose a new intelligent agent based IDS using Fuzzy Rough Set based outlier detection and Fuzzy Rough set based SVM. In this proposed model we intorduced two different inteligent agents namely feature selection agent to select the required feature set using fuzzy rough sets and decision making agent manager for making final decision. Moreover, we have introduced fuzzy rough set based outlier detection algorithm to detect outliers. We have also adopted Fuzzy Rough based SVM in our system to classify and detect anomalies efficiently. Finally, we have used KDD Cup 99 data set for our experiment, the experimental result show that the proposed intelligent agent based model improves the overall accuracy and reduces the false alarm rate.