HFFPNN classifier: a hybrid approach for intrusion detection based OPSO and hybridization of feed forward neural network (FFNN) and probabilistic neural network (PNN)

Abstract

Quick increase in web and system advancements has prompted significant increase in number of attacks and intrusions. Identification and prevention of these attacks has turned into an important part of security. Intrusion detection framework is one of the vital approaches to accomplish high security in computer systems and used to oppose attacks. Intrusion detection frameworks have reviled of dimensionality which tends to build time complexity and reduce resource use. Therefore, it is desirable that critical components of information must be examined by interruption detection framework to decrease dimensionality. These reduced features are then fed to a HFFPNN for training and testing on NSL-KDD dataset. HFFPNN is the hybridization of feed forward neural network (FFNN) and probabilistic neural network (PNN). Pre-processing of NSL-KDD dataset has been done to convert string attributes into numeric attributes before training. Comparisons with recent and relevant approaches are also tabled. Experimental results show the prominence of HFFPNN technique over the existing techniques in terms of intrusion detection classification. Therefore, the scope of this study has been expanded to encompass hybrid classifiers.