Unsupervised Genetic Algorithm Deployed for Intrusion Detection

Bankovic, Zorana; Bojanic, Slobodan; Nieto-Taladriz García, Octavio y Badii, Atta (2008). Unsupervised Genetic Algorithm Deployed for Intrusion Detection. En: "3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS'08", 24/09/2008-26/09/2008, Burgos, España.

Descripción

Título: Unsupervised Genetic Algorithm Deployed for Intrusion Detection
Autor/es:
  • Bankovic, Zorana
  • Bojanic, Slobodan
  • Nieto-Taladriz García, Octavio
  • Badii, Atta
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS'08
Fechas del Evento: 24/09/2008-26/09/2008
Lugar del Evento: Burgos, España
Título del Libro: Proceedings of the 3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS'08. Hybrid Artificial Intelligence Systems
Fecha: 2008
Volumen: 5271
Materias:
Palabras Clave Informales: intrusion detection, genetic algorithm, unsupervised
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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URL Oficial: http://www.springerlink.com/content/k8v2874065856h23/

Resumen

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machinelearning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method.

Más información

ID de Registro: 4320
Identificador DC: http://oa.upm.es/4320/
Identificador OAI: oai:oa.upm.es:4320
Depositado por: Memoria Investigacion
Depositado el: 24 Sep 2010 10:10
Ultima Modificación: 20 Abr 2016 13:36
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