Intrusion Detection and Classification of Attacks in High-Level Network Protocols Using Recurrent Neural Networks

  • Vicente Alarcon-Aquino
  • Carlos A. Oropeza-Clavel
  • Jorge Rodriguez-Asomoza
  • Oleg Starostenko
  • Roberto Rosas-Romero
Conference paper

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.

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Vicente Alarcon-Aquino
    • 1
  • Carlos A. Oropeza-Clavel
    • 1
  • Jorge Rodriguez-Asomoza
    • 1
  • Oleg Starostenko
    • 1
  • Roberto Rosas-Romero
    • 1
  1. 1.Department of Computing, Electronics, and Mechatronics, Communication and Signal Processing Research GroupUniversidad de las Américas PueblaPueblaMexico

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