Implementation of Database intrusion Detection - Couverture souple

Yadav, Jay Kant Pratap Singh

9783330079014: Implementation of Database intrusion Detection

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ISBN 10 : 3330079010 ISBN 13 : 9783330079014

Editeur : LAP LAMBERT Academic Publishing, 2017

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Here, we propose data mining approach for database intrusion detection. In each database, there are a few attributes or columns that are more important or sensitive to be tracked or sensed for malicious modifications as compared to the other attributes. Our approach concentrates on mining pre-write as well as post-write data dependencies among the important or sensitive data items in relational database. By data dependency we refer to the data access correlations between two or more data items. These dependencies are generated in the form of association rules i.e. before one data item is updated in the data base what other data items probably need to be read or write and after this data item is updated what other data items are most likely to be updated by the same transaction. Any transaction that does not follow these dependency rules are identified as malicious. We also suggest removal of redundant rules in our proposed algorithm to minimize the number of comparisons during detection phase. We compare our proposed approach with existing approach on various performance evaluation metrics and analyze the results

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� propos de l?auteur

Jay Kant Pratap Singh Yadav Completed his B.Tech(Computer Science & Engineering), M.Tech (Computer Engineering) from Sardar Vallabhbhai National Institute of Technology,Surat(India). He has about 13 years teaching experience in various technical colleges of india. His research interest is in Machine Learning,Soft Computing,Data Mining .

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2017
Langue: anglais
ISBN 10: 3330079010
ISBN 13: 9783330079014
Reliure: Broch�
Nombre de pages: 64
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Implementation of Database intrusion Detection

Jay Kant Pratap Singh Yadav

Edit� par LAP LAMBERT Academic Publishing Apr 2017, 2017

ISBN 10 : 3330079010 ISBN 13 : 9783330079014

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Here, we propose data mining approach for database intrusion detection. In each database, there are a few attributes or columns that are more important or sensitive to be tracked or sensed for malicious modifications as compared to the other attributes. Our approach concentrates on mining pre-write as well as post-write data dependencies among the important or sensitive data items in relational database. By data dependency we refer to the data access correlations between two or more data items. These dependencies are generated in the form of association rules i.e. before one data item is updated in the data base what other data items probably need to be read or write and after this data item is updated what other data items are most likely to be updated by the same transaction. Any transaction that does not follow these dependency rules are identified as malicious. We also suggest removal of redundant rules in our proposed algorithm to minimize the number of comparisons during detection phase. We compare our proposed approach with existing approach on various performance evaluation metrics and analyze the results 64 pp. Englisch. N� de r�f. du vendeur 9783330079014

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Implementation of Database intrusion Detection

Jay Kant Pratap Singh Yadav

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Yadav Jay Kant Pratap SinghJay Kant Pratap Singh Yadav Completed his B.Tech(Computer Science & Engineering), M.Tech (Computer Engineering) from Sardar Vallabhbhai National Institute of Technology,Surat(India). He has about 13 years t. N� de r�f. du vendeur 151236808

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Implementation of Database intrusion Detection

Jay Kant Pratap Singh Yadav

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Implementation of Database intrusion Detection

Jay Kant Pratap Singh Yadav

Edit� par LAP LAMBERT Academic Publishing Apr 2017, 2017

ISBN 10 : 3330079010 ISBN 13 : 9783330079014

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Here, we propose data mining approach for database intrusion detection. In each database, there are a few attributes or columns that are more important or sensitive to be tracked or sensed for malicious modifications as compared to the other attributes. Our approach concentrates on mining pre-write as well as post-write data dependencies among the important or sensitive data items in relational database. By data dependency we refer to the data access correlations between two or more data items. These dependencies are generated in the form of association rules i.e. before one data item is updated in the data base what other data items probably need to be read or write and after this data item is updated what other data items are most likely to be updated by the same transaction. Any transaction that does not follow these dependency rules are identified as malicious. We also suggest removal of redundant rules in our proposed algorithm to minimize the number of comparisons during detection phase. We compare our proposed approach with existing approach on various performance evaluation metrics and analyze the resultsVDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 64 pp. Englisch. N� de r�f. du vendeur 9783330079014

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Implementation of Database intrusion Detection

Jay Kant Pratap Singh Yadav

Edit� par LAP LAMBERT Academic Publishing, 2017

ISBN 10 : 3330079010 ISBN 13 : 9783330079014

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Here, we propose data mining approach for database intrusion detection. In each database, there are a few attributes or columns that are more important or sensitive to be tracked or sensed for malicious modifications as compared to the other attributes. Our approach concentrates on mining pre-write as well as post-write data dependencies among the important or sensitive data items in relational database. By data dependency we refer to the data access correlations between two or more data items. These dependencies are generated in the form of association rules i.e. before one data item is updated in the data base what other data items probably need to be read or write and after this data item is updated what other data items are most likely to be updated by the same transaction. Any transaction that does not follow these dependency rules are identified as malicious. We also suggest removal of redundant rules in our proposed algorithm to minimize the number of comparisons during detection phase. We compare our proposed approach with existing approach on various performance evaluation metrics and analyze the results. N� de r�f. du vendeur 9783330079014

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