Intrusion Detection and Classification of Attacks in High-Level Network Protocols Using Recurrent Neural Networks
BibTeX
@MISC{Alarcon-aquino_intrusiondetection,
author = {Vicente Alarcon-aquino and Carlos A. Oropeza-clavel and Jorge Rodriguez-asomoza and Oleg Starostenko and Roberto Rosas-romero},
title = {Intrusion Detection and Classification of Attacks in High-Level Network Protocols Using Recurrent Neural Networks},
year = {}
}
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Abstract
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. I.
