Unsupervised Genetic Algorithm Deployed for Intrusion Detection

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

Description

Title: Unsupervised Genetic Algorithm Deployed for Intrusion Detection
Author/s:
  • Bankovic, Zorana
  • Bojanic, Slobodan
  • Nieto-Taladriz García, Octavio
  • Badii, Atta
Item Type: Presentation at Congress or Conference (Article)
Event Title: 3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS'08
Event Dates: 24/09/2008-26/09/2008
Event Location: Burgos, España
Title of Book: Proceedings of the 3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS'08. Hybrid Artificial Intelligence Systems
Date: 2008
Volume: 5271
Subjects:
Freetext Keywords: intrusion detection, genetic algorithm, unsupervised
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 4320
DC Identifier: http://oa.upm.es/4320/
OAI Identifier: oai:oa.upm.es:4320
Official URL: http://www.springerlink.com/content/k8v2874065856h23/
Deposited by: Memoria Investigacion
Deposited on: 24 Sep 2010 10:10
Last Modified: 20 Apr 2016 13:36
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