A feature reduced intrusion detection system using ANN classifier

Rapid increase in internet and network technologies has led to considerable increase in number of attacks and intrusions. Detection and prevention of these attacks has become an important part of security. Intrusion detection system is one of the important ways to achieve high security in computer networks and used to thwart different attacks. Intrusion detection systems have curse of dimensionality which tends to increase time complexity and decrease resource utilization. As a result, it is desirable that important features of data must be analyzed by intrusion detection system to reduce dimensionality. This work proposes an intelligent system which first performs feature ranking on the basis of information gain and correlation. Feature reduction is then done by combining ranks obtained from both information gain and correlation using a novel approach to identify useful and useless features. These reduced features are then fed to a feed forward neural network for training and testing on KDD99 dataset. Pre-processing of KDD-99 dataset has been done to normalize number of instances of each class before training. The system then behaves intelligently to classify test data into attack and non-attack classes. The aim of the feature reduced system is to achieve same degree of performance as a normal system. The system is tested on five different test datasets and both individual and average results of all datasets are reported. Comparison of proposed method with and without feature reduction is done in terms of various performance metrics. Comparisons with recent and relevant approaches are also tabled. Results obtained for proposed method are really encouraging.