Memorias de investigación
Ponencias en congresos:
Unsupervised Genetic Algorithm Deployed for Intrusion Detection
Año:2008
Áreas de investigación
  • Industria electrónica
Datos
Descripción
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.
Internacional
Si
Nombre congreso
3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS'08
Tipo de participación
960
Lugar del congreso
Burgos (España)
Revisores
Si
ISBN o ISSN
DOI
Fecha inicio congreso
24/09/2008
Fecha fin congreso
26/09/2008
Desde la página
0
Hasta la página
0
Título de las actas
Procedings of the 3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS'08
Esta actividad pertenece a memorias de investigación
Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Laboratorio de Sistemas Integrados (LSI)
  • Departamento: Ingeniería Electrónica