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Aggiungi al carrelloPAP. Condizione: New. Alobaidi, Ghada (illustratore). New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659928658 ISBN 13: 9783659928659
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 148 pages. 8.66x5.91x0.34 inches. In Stock.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659697176 ISBN 13: 9783659697173
Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Partridge Publishing Singapore, 2018
ISBN 10: 1543747809 ISBN 13: 9781543747805
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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Editore: LAP LAMBERT Academic Publishing, 2016
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Da: Mispah books, Redhill, SURRE, Regno Unito
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Aggiungi al carrellopaperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Lingua: Inglese
Editore: Partridge Publishing Singapore, 2018
ISBN 10: 1543747809 ISBN 13: 9781543747805
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
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Aggiungi al carrelloPAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 180.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. Print on Demand pp. 180.
Da: Biblios, Frankfurt am main, HESSE, Germania
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 180.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659928658 ISBN 13: 9783659928659
Da: moluna, Greven, Germania
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bin Mohd Ali HairuddinProfessor Hairuddin Mohd Ali is a lecturer and Educational Leadership consultant at IIUM in Malaysia. Dr. Inas Zulkifli is a consultant in Leadership and Management. Assistant Professor Dr. Lasisi Abass Ayodele .
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Mai 2015, 2015
ISBN 10: 3659697176 ISBN 13: 9783659697173
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 61,90
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659697176 ISBN 13: 9783659697173
Da: moluna, Greven, Germania
EUR 50,66
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Aggiungi al carrelloCondizione: 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 .
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Mai 2015, 2015
ISBN 10: 3659697176 ISBN 13: 9783659697173
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 61,90
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659697176 ISBN 13: 9783659697173
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 61,90
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: 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.