ICTACT Journal on Soft Computing

S. Devaraju ¹, S. Ramakrishnan ²

Affiliations
1 Department of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, IN

Abstract

Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Radial Basis Neural Network (RBNN). The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

Keywords

Intrusion Detection, Neural Networks, KDD Cup 1999 Dataset, MATLAB.

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