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 results show that the Jordan network outperforms the Elman network predicting correctly queries of the length up to ten.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Valeur, F., Mutz, D., Vigna, G.: A Learning-Based Approach to the Detection of SQL Attacks. In: Proceedings of the Conference on Detection of Intrusions and Malware and Vulnerability Assessment, Austria (2005)
Kruegel, C., Vigna, G.: Anomaly Detection of Web-based Attacks. In: Proceedings of the 10th ACM Conference on Computer and Communication Security, pp. 251–261 (2003)
Almgren, M., Debar, H., Dacier, M.: A lightweight Tool for Detecting Web Server Attacks. In: Proceedings of the ISOC Symposium on Network and Distributed Systems Security (2000)
Tan, K.M.C., Killourhy, K.S., Maxion, R.A.: Undermining an Anomaly-Based Intrusion Detection System Using Common Exploits. In: Wespi, A., Vigna, G., Deri, L. (eds.) RAID 2002. LNCS, vol. 2516, pp. 54–73. Springer, Heidelberg (2002)
Nunn, I., White, T.: The Application of Antigenic Search Techniques to Time Series Forecasting. In: Proceedings of the Genetic and Evolutionary Computation Conference, USA (2005)
Kendall, M., Ord, J.: Time Series, Third Edition (1999)
Pollock, D.: A Handbook of Time-Series Analysis, Signal Processing and Dynamics. Academic Press, London (1999)
Lin, T., Horne, B.G., Tino, P., Giles, C.L.: Learning Long-Term Dependencies in NARX Recurrent Neural Networks. IEEE Transactions on Neural Networks, 1329 (1996)
Drake, P.R., Miller, K.A.: Improved Self-Feedback Gain in the Context Layer of a Modified Elman Neural Network. Mathematical and Computer Modelling of Dynamical Systems, 307–311 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Skaruz, J., Seredynski, F. (2007). Recurrent Neural Networks on Duty of Anomaly Detection in Databases. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-72395-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
eBook Packages: Computer ScienceComputer Science (R0)