Intrusion detection and classification of attacks in high-level network protocols using Recurrent Neural Networks. Alarcon-Aquino, V., Oropeza-Clavel, C., A., Rodriguez-Asomoza, J., Starostenko, O., & Rosas-Romero, R. In Novel Algorithms and Techniques in Telecommunications and Networking, pages 129-134, 2010. ![Intrusion detection and classification of attacks in high-level network protocols using Recurrent Neural Networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex This paper presents an application-based model for classifying and identifying attacks in a communications network and therefore guarantees its safety from HTTP protocol-based malicious commands. The proposed model is based on a recurrent neural network architecture and it is therefore suitable to work online and for analyzing non-linear patterns in real time to self-adjust to changes in its input environment. Three different neural network-based systems have been modelled and simulated for comparison purposes in terms of overall performance: a Feed-forward Neural Network, an Elman Network, and a Recurrent Neural Network. Simulation results show that the latter possesses a greater capacity than either of the others for the correct identification and classification of HTTP attacks, and it also reaches a result at a great speed, its somewhat taxing computing requirements notwithstanding. © 2010 Springer Science+Business Media B.V.
@inproceedings{
title = {Intrusion detection and classification of attacks in high-level network protocols using Recurrent Neural Networks},
type = {inproceedings},
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abstract = {This paper presents an application-based model for classifying and identifying attacks in a communications network and therefore guarantees its safety from HTTP protocol-based malicious commands. The proposed model is based on a recurrent neural network architecture and it is therefore suitable to work online and for analyzing non-linear patterns in real time to self-adjust to changes in its input environment. Three different neural network-based systems have been modelled and simulated for comparison purposes in terms of overall performance: a Feed-forward Neural Network, an Elman Network, and a Recurrent Neural Network. Simulation results show that the latter possesses a greater capacity than either of the others for the correct identification and classification of HTTP attacks, and it also reaches a result at a great speed, its somewhat taxing computing requirements notwithstanding. © 2010 Springer Science+Business Media B.V.},
bibtype = {inproceedings},
author = {Alarcon-Aquino, Vicente and Oropeza-Clavel, Carlos A. and Rodriguez-Asomoza, Jorge and Starostenko, Oleg and Rosas-Romero, Roberto},
doi = {10.1007/978-90-481-3662-9-21},
booktitle = {Novel Algorithms and Techniques in Telecommunications and Networking}
}
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