A Model To Detetct DOS Using Data Mining Classification Algorithms - Softcover

Ali, Inas; Hassan, Soukaena

Softcover

ISBN 10: 3659697176 ISBN 13: 9783659697173

Verlag: LAP LAMBERT Academic Publishing, 2015

Inhaltsangabe

This work proposes an Intrusion Detection Model (IDM) for detection of intrusion attempts caused by worms. The proposal is a hybrid IDM since it considers features of both network packets and host that are sensitive to worms. The proposed HybD (Hybrid Dataset) dataset, which is composed of the 10% KDD'99 (Knowledge Discovery in Databases) dataset features and the suggested host-based features, is used to build and test the proposed model. Both of misuse and anomaly detection approaches are used. The hybrid IDM has been designed using Data Mining (DM) methods that for their ability to detect new intrusions accurately and automatically, also it can process large amount of data, and it is more likely to discover the ignored and hidden information. Interactive Dichotomizer 3 classifier (ID3) and Naïve Bayesian Classifier (NB) are used to build and verify the validity of the proposed model in term of classifier accuracy. The results of implementing the proposed model show that accuracy of NB classifier is generally higher than that of ID3 classifier with the four sets of features.

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Über die Autorin bzw. den Autor

This book written by Inas Ali who is an assistance lecturer at Computer Science Department in Baghdad University. She has got BcS degree in computer science from Baghdad University in 2003, and the Master degree in computer science from Iraqi Commission for Computers and Informatics in 2013.

Details zur Ausgabe

• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsdatum: 2015
• Sprache: Englisch
• ISBN 10: 3659697176
• ISBN 13: 9783659697173
• Einband: Taschenbuch
• Anzahl der Seiten: 132
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: Edicoes Nosso Conhecimento; customerservice@vdm-vsg.de; ; SIA; Riga; 1039; Litauen