An Intelligent Network Intrusion Detection and Prevention System: The way forward to integrate Data mining and Knowledge Based System for network intrusion detection and prevention - Couverture souple

Chiche, Alebachew; Meshesha, Million

 
9786202076227: An Intelligent Network Intrusion Detection and Prevention System: The way forward to integrate Data mining and Knowledge Based System for network intrusion detection and prevention
Couverture souple
ISBN 10 : 6202076224 ISBN 13 : 9786202076227
Editeur : LAP LAMBERT Academic Publishing, 2017

Synopsis

With the wide use of Internet and network connectivity, it is important to prevent unauthorized access to system resources and data. In this work, an intelligent network intrusion detection and prevention system is presented for detecting and preventing network attacks that incorporates a knowledge based system and data mining techniques. Hybrid data mining process model is followed for data mining tasks to extract hidden knowledge from KDDCup’99 intrusion dataset. J48 decision tree, JRip rule induction, Naïve Bayes and Multilayer Perceptron (MLP) Neural Network are adopted to construct a predictive model on total datasets of 63, 661 instances. This work supports network administrators to fill the knowledge gap they have to detect and prevent network attacks efficiently and effectively. Experimental result shows that, the proposed system performs 91.43 percent and 83 percent detection accuracy and user acceptance, respectively. The system cannot update the knowledge of prevention techniques automatically which need further researches.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

With the wide use of Internet and network connectivity, it is important to prevent unauthorized access to system resources and data. In this work, an intelligent network intrusion detection and prevention system is presented for detecting and preventing network attacks that incorporates a knowledge based system and data mining techniques. Hybrid data mining process model is followed for data mining tasks to extract hidden knowledge from KDDCup’99 intrusion dataset. J48 decision tree, JRip rule induction, Naïve Bayes and Multilayer Perceptron (MLP) Neural Network are adopted to construct a predictive model on total datasets of 63, 661 instances. This work supports network administrators to fill the knowledge gap they have to detect and prevent network attacks efficiently and effectively. Experimental result shows that, the proposed system performs 91.43 percent and 83 percent detection accuracy and user acceptance, respectively. The system cannot update the knowledge of prevention techniques automatically which need further researches.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2017
Langue
anglais
ISBN 10 
6202076224
ISBN 13 
9786202076227
Reliure
Broché
Nombre de pages
144
Coordonnées du fabricant
non disponible
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