EFFICIENT INTRUSION DETECTION SYSTEM MODELS

This paper investigates the suitability of various techniques to model efficient intrusion detection systems. We briefly describe some of the latest developments made to increase the efficiency and accuracy of intrusion detection using support vector machines(SVMs), artificial neural networks (ANNs) and linear genetic programming (LGP). One approach focuses on reducing training time of SVMs using clustering tree analysis called CT-SVM. Another approach called DT-SVM combines Decision tree and SVM to maximize the detection accuracy. Third uses LGP with feature selection and ranking algorithm. Fourth uses ANN with Back propagation Algorithm. Here we will compare the performances of all the above approaches.