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Intrusion Detection Using Cost-Sensitive Classification

Auteur(s)
Aikaterini Mitrokotsa*
Dimitrakakis, Christos 
Institut d'informatique 
Christos Douligeris
Date de parution
2009
In
Proceedings of the 3rd European Conference on Computer Network Defense
Lecture Notes in Electrical Engineering
De la page
35
A la page
47
Mots-clés
  • False Alarm
  • Gaussian Mixture Model
  • Intrusion Detection
  • Test Dataset
  • Intrusion Detection System
  • False Alarm

  • Gaussian Mixture Mode...

  • Intrusion Detection

  • Test Dataset

  • Intrusion Detection S...

Résumé
Intrusion Detection is an invaluable part of computer networks defense. An important consideration is the fact that raising false alarms carries a significantly lower cost than not detecting attacks. For this reason, we examine how cost-sensitive classification methods can be used in Intrusion Detection systems. The performance of the approach is evaluated under different experimental conditions, cost matrices and different classification models, in terms of expected cost, as well as detection and false alarm rates. We find that even under unfavourable conditions, cost-sensitive classification can improve performance significantly, if only slightly.
Identifiants
https://libra.unine.ch/handle/123456789/30998
_
10.1007/978-0-387-85555-4_3
_
9780387855547
9780387855554
Type de publication
book part
Dossier(s) à télécharger
 main article: 0807.2043.pdf (220.55 KB)
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