System for Intrusion Detection with Artificial Neural Network

Jose Ernesto Luna, Anabelem Soberanes Martín, Cristina Juárez Landín

Abstract

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.

References

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Paper Citation


in Harvard Style

Luna J., Martín A. and Landín C. (2015). System for Intrusion Detection with Artificial Neural Network . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 470-475. DOI: 10.5220/0005256704700475


in Bibtex Style

@conference{icaart15,
author={Jose Ernesto Luna and Anabelem Soberanes Martín and Cristina Juárez Landín},
title={System for Intrusion Detection with Artificial Neural Network},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2015},
pages={470-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005256704700475},
isbn={978-989-758-074-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - System for Intrusion Detection with Artificial Neural Network
SN - 978-989-758-074-1
AU - Luna J.
AU - Martín A.
AU - Landín C.
PY - 2015
SP - 470
EP - 475
DO - 10.5220/0005256704700475