An Intelligent Agent Based Intrusion Detection System Using Fuzzy Rough Set Based Outlier Detection

• DOI: 10.47893/ijess.2011.1018
• Publication Date: 2021-01-12T08:46:22Z
• Authors: N. Jaisankar; M Ganapathy; A Kannan; K Anand
• Keywords: Feature (linguistics); False positive rate

Since existing Intrusion Detection Systems (IDS) including misuse detection and anomoly are generally incapable of detecting new type attacks. However, all these systems capable intruders with high false alarm rate. It is an urgent need to develop IDS very rate low False To satisfy this we propose a intelligent agent based using Fuzzy Rough Set outlier set SVM. In proposed model intorduced two different inteligent agents namely feature selection select the required fuzzy rough sets decision making manager for final decision. Moreover, have introduced algorithm detect outliers. We also adopted SVM in our system classify anomalies efficiently. Finally, used KDD Cup 99 data experiment, experimental result show that improves overall accuracy reduces