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

Alebachew Chiche (u. a.)

ISBN 10: 6202076224 / ISBN 13: 9786202076227

Published by LAP LAMBERT Academic Publishing, 2018

About this Item

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 | Alebachew Chiche (u. a.) | Taschenbuch | 144 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786202076227 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 110858978

Bibliographic Details

• Title: 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
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 6202076224
• ISBN 13: 9786202076227
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 233 grams
• Seller catalogs:
  • Bücher

About this title

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.

About the Author

Alebachew Chiche received a B.Sc. degree in Information Systems from Hawassa University in 2012 and his M.Sc. degrees in computer networking from Jimma University in 2016. He is a Lecturer of Information Systems at Mizan-Tepi University. He is currently serving a term as Head of the Department of Information Systems.