抽象的

Soft Computing Based Classification Technique Using KDD 99 Data Set for Intrusion Detection System

Mr. Suresh kashyap ,Ms. Pooja Agrawal, Mr.Vikas Chandra Pandey, Mr. Suraj Prasad Keshri

An intrusion detection system (IDS) inspects all inbound and outbound network activity and identifies suspicious patterns that may indicate a network or system attack from someone attempting to break into or compromise a system. Soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems. Knowledge Discovery in Databases (KDD) is the automated discovery of patterns and relationships in large databases. In this paper we are going to preprocess the different of KDD cup 99 data set. The two algorithms Error back propagation (EBP) which is the most used training algorithm for feedforwrd artificial neural networks (FFANNs) and the Radial basis function (RBF) neural network which is based on supervised learning are compared .After the process we give result that Radial basis function (RBF) is better than Error back propagation (EBP) .For comparison we used MATLAB tool.