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Network Anomaly Classification by Support Vector Classifiers Ensemble and Non-linear Projection Techniques

  • Eduardo de la Hoz
  • Andrés Ortiz
  • Julio Ortega
  • Emiro de la Hoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)

Abstract

Network anomaly detection is currently a challenge due to the number of different attacks and the number of potential attackers. Intrusion detection systems aim to detect misuses or network anomalies in order to block ports or connections, whereas firewalls act according to a predefined set of rules. However, detecting the specific anomaly provides valuable information about the attacker that may be used to further protect the system, or to react accordingly. This way, detecting network intrusions is a current challenge due to growth of the Internet and the number of potential intruders. In this paper we present an intrusion detection technique using an ensemble of support vector classifiers and dimensionality reduction techniques to generate a set of discriminant features. The results obtained using the NSL-KDD dataset outperforms previously obtained classification rates.

Keywords

Feature Selection Intrusion Detection Anomaly Detection Intrusion Detection System Attack Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Eduardo de la Hoz
    • 1
    • 3
  • Andrés Ortiz
    • 2
  • Julio Ortega
    • 1
  • Emiro de la Hoz
    • 1
    • 3
  1. 1.Computer Architecture and Technology DepartmentCITIC University of GranadaGranadaSpain
  2. 2.Department of Communications EngineeringUniversity of MálagaMálagaSpain
  3. 3.Systems Engineering ProgramUniversidad de la CostaBarranquillaColombia

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