Abstract
We have proposed an intelligent intrusion detection system (IIDS) that
used soft computing approaches. The current IIDS uses the support vector
machine (SVM) or the CLustering In QUEst (CLIQUE). To evaluate IIDS, we
need to use real world network traffic data because each IIDS is used at
various environments. That is, it is very important that we use the real
world network traffic data for training and evaluating. Therefore, we
make software that aids us to generate training patterns from real world
network traffic. Then our IIDS is trained by using of the training
patterns. In computer simulations, we show that when the real world
network traffic data were used for training, the classification
performance is better than when the DARPA data were used.
