Comparative analysis of backpropagation algorithm variants for network intrusion detection

Abstract

The system security has turned into an extremely critical worry as system assaults have been extending with the ascent of hacking devices, inconvenience of systems and interruptions in number and brutality. This paper is centered around interruption identification by utilizing Multilayer Perceptron (MLP) with various calculation of backpropagation neural network. In this paper, performance of various backpropagation algorithms has been evaluated using KDDCup99 dataset. The dataset has been preprocessed to be made suitable for neural network input and the input set and target set are separated. The modified dataset has been used to evaluate the performance of BFGS Quasi-Newton, Levenberg-Marquardt, and Gradient Descent with Adaptive backpropagation algorithm. Different performance parameters such as mean square error, attack detection rate, recall rate, precision rate, epochs has been used for the algorithm comparison. Based on the evaluation results, the research purposes Levenberg-Marquardt backpropagation algorithm to be the best performing and efficient algorithm for the network intrusion detection for KDD Cup dataset. Different classes of attacks have been also determined comparing the output values obtained with the target set.