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Neural Analysis of HTTP Traffic for Web Attack Detection

  • David AtienzaEmail author
  • Álvaro Herrero
  • Emilio Corchado
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 369)

Abstract

Hypertext Transfer Protocol (HTTP) is the cornerstone for information exchanging over the World Wide Web by a huge variety of devices. It means that a massive amount of information travels over such protocol on a daily basis. Thus, it is an appealing target for attackers and the number of web attacks has increased over recent years. To deal with this matter, neural projection architectures are proposed in present work to analyze HTTP traffic and detect attacks over such protocol. By the advanced and intuitive visualization facilities obtained by neural models, the proposed solution allows providing an overview of HTTP traffic as well as identifying anomalous situations, responding to the challenges presented by volume, dynamics and diversity of that traffic. The applied dimensionality reduction based on Neural Networks, enables the most interesting projections of an HTTP traffic dataset to be extracted.

Keywords

Intrusion detection HTTP Artificial neural networks Exploratory projection pursuit 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • David Atienza
    • 1
    Email author
  • Álvaro Herrero
    • 1
  • Emilio Corchado
    • 2
  1. 1.Department of Civil EngineeringUniversity of Burgos Spain C/Francisco de Vitoria s/nBurgosSpain
  2. 2.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

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