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Authors: Jaroslaw Skaruz 1 ; Franciszek Seredynski 2 and Pascal Bouvry 3

Affiliations: 1 Institute of Computer Science, University of Podlasie, Poland ; 2 Institute of Computer Science, Polish Academy of Sciences, Poland ; 3 Faculty of Sciences, Technology and Communication, University of Luxembourg, Luxembourg

ISBN: 978-972-8865-89-4

Keyword(s): Recurrent neural network, Elman, Jordan, database security, anomaly detection.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: In the paper we present a new approach based on application of neural networks to detect SQL attacks. SQL attacks are those attacks that take advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker, we divide SQL statements into tokens and pass them to our detection system, which predicts the next token, taking into account previously seen tokens. In the learning phase tokens are passed to recurrent neural network (RNN) trained by backpropagation through time (BPTT) algorithm. Teaching data are shifted by one token forward in time with relation to input. The purpose of the testing phase is to predict the next token in the sequence. All experiments were conducted on Jordan and Elman networks using data gathered from PHP Nuke portal. Experimental resu lts show that the Jordan network outperforms the Elman network predicting correctly queries of the length up to ten. (More)

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Paper citation in several formats:
Skaruz J.; Seredynski F.; Bouvry P. and (2007). RECURRENT NEURAL NETWORKS APPROACH TO THE DETECTION OF SQL ATTACKS.In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-972-8865-89-4, pages 191-197. DOI: 10.5220/0002352901910197

@conference{iceis07,
author={Jaroslaw Skaruz and Franciszek Seredynski and Pascal Bouvry},
title={RECURRENT NEURAL NETWORKS APPROACH TO THE DETECTION OF SQL ATTACKS},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2007},
pages={191-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002352901910197},
isbn={978-972-8865-89-4},
}

TY - CONF

JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - RECURRENT NEURAL NETWORKS APPROACH TO THE DETECTION OF SQL ATTACKS
SN - 978-972-8865-89-4
AU - Skaruz, J.
AU - Seredynski, F.
AU - Bouvry, P.
PY - 2007
SP - 191
EP - 197
DO - 10.5220/0002352901910197

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