alebachew chiche (6 resultados)

Paperback. Condición: Brand New. 144 pages. 8.66x5.91x0.33 inches. In Stock.

Taschenbuch. Condición: Neu. 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.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 144 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chiche AlebachewAlebachew 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 Mi.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 144 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.