Synopsis
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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A Model To Detetct DOS Using Data Mining Classification Algorithms
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LAP LAMBERT Academic Publishing Mai 2015, 2015
ISBN 10: 3659697176
ISBN 13: 9783659697173
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 132 pp. Englisch. Seller Inventory # 9783659697173
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A Model To Detetct DOS Using Data Mining Classification Algorithms
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ISBN 13: 9783659697173
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ali InasThis 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 . Seller Inventory # 158428838
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A Model To Detetct DOS Using Data Mining Classification Algorithms
Published by
LAP LAMBERT Academic Publishing Mai 2015, 2015
ISBN 10: 3659697176
ISBN 13: 9783659697173
New
Taschenbuch
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch. Seller Inventory # 9783659697173
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A Model To Detetct DOS Using Data Mining Classification Algorithms
Published by
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659697176
ISBN 13: 9783659697173
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783659697173
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A Model To Detetct DOS Using Data Mining Classification Algorithms
Published by
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659697176
ISBN 13: 9783659697173
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Taschenbuch. Condition: Neu. A Model To Detetct DOS Using Data Mining Classification Algorithms | Inas Ali (u. a.) | Taschenbuch | 132 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659697173 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 104646856