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Synopsis
Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.
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About the Author
Roshan Chitrakar, born in 1967, is a Ph.D. in Information Security of Wuhan University, China. His areas of interests are Data Mining, Programming Languages, Software Engineering, Databases etc. Starting IT career in 1987 at National Computer Centre, Nepal, he now works as an Associate Professor at Nepal College of Information Technology, Nepal.
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Hybrid Intrusion Detection
Published by
LAP LAMBERT Academic Publishing Nov 2016, 2016
ISBN 10: 365997921X
ISBN 13: 9783659979217
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the 'Clustering-Outlier Detection' algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs 'Candidate Support Vector Selection' algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference. 140 pp. Englisch. Seller Inventory # 9783659979217
Quantity: 2 available
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Hybrid Intrusion Detection : Clustering-Outlier and Incremental SVM
Published by
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 365997921X
ISBN 13: 9783659979217
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the 'Clustering-Outlier Detection' algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs 'Candidate Support Vector Selection' algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference. Seller Inventory # 9783659979217
Quantity: 1 available
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Hybrid Intrusion Detection
Published by
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 365997921X
ISBN 13: 9783659979217
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chitrakar RoshanRoshan Chitrakar, born in 1967, is a Ph.D. in Information Security of Wuhan University, China. His areas of interests are Data Mining, Programming Languages, Software Engineering, Databases etc. Starting IT career in . Seller Inventory # 158607248
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Hybrid Intrusion Detection
Published by
LAP LAMBERT Academic Publishing Nov 2016, 2016
ISBN 10: 365997921X
ISBN 13: 9783659979217
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the ¿Clustering-Outlier Detection' algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs ¿Candidate Support Vector Selection¿ algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.Books on Demand GmbH, Überseering 33, 22297 Hamburg 140 pp. Englisch. Seller Inventory # 9783659979217
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Hybrid Intrusion Detection: Clustering-Outlier and Incremental SVM
Published by
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 365997921X
ISBN 13: 9783659979217
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Seller: Revaluation Books, Exeter, United Kingdom
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Paperback. Condition: Brand New. 140 pages. 8.66x5.91x0.32 inches. In Stock. Seller Inventory # 365997921X
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