Anabelem Soberanes Martín

System for Intrusion Detection with Artificial Neural Network

With the rapid expansion of computer networks during the past decade, security has become a crucial issue for computer systems. Different soft-computing based methods have been proposed in recent years for the development of intrusion detection systems. This paper presents a neural network approach to intrusion detection. A Multi-Layer Perceptron (MLP) is used for intrusion detection based on an off-line analysis approach. While most of the previous studies have focused on classification of records in one of the two general classes - normal and attack, this research aims to solve a multi class problem in which the type of attack is also detected by the neural network. Different neural network structures are analyzed to find the optimal neural network with regards to the number of hidden layers. An early stopping validation method is also applied in the training phase to increase the generalization capability of the neural network. The results show that the designed system is capable of classifying records with about 91% accuracy with two hidden layers of neurons in the neural network and 87% accuracy with one hidden layer.