Classification of data mining techniques in intrusion detection: Classification Techniques in Data Mining - Tapa blanda

Mishra, Gaurav; Ahuja, Gulshan

9783659442155: Classification of data mining techniques in intrusion detection: Classification Techniques in Data Mining

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ISBN 10: 3659442151 ISBN 13: 9783659442155

Editorial: LAP LAMBERT Academic Publishing, 2013

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Data mining is the process of gathering, searching, and analyzing a large amount of raw data, as to discover patterns, relationships and behavior of data. There are large numbers of algorithms for classification of data mining. Single algorithm is not efficient for classification of data and recognize their pattern and behavior .There is a key term known as ensemble learning which means Combining two or more classifiers for efficient result. I have used the KDD’99 dataset for the experiment which have 41 features labeled either as normal or as an attack. In this book I have represented how graphical machine learning tool weka can be used for data mining and how ensemble learning can be implemented using weka.I have used three classifiers with the Bagging and Boosting ensemble learning approach which are complementary na�ve bayes and two are rule based classifiers, part and jrip. My experiment shows that bagging improves the efficiency of the rule based classifiers as well as of na�ve Bayes. However, the rule based classifiers become more efficient with bagging and boosting techniques.

Biograf�a del autor

Gaurav Mishra is born in India and has received His Bachelor’s degree in Computer Science & Engg.From Malout Institute of Management & Information Technology, Malout India. His areas of interest are Network Security, Java programming & Data Mining.

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EditorialLAP LAMBERT Academic Publishing
A�o de publicaci�n2013
ISBN 10 3659442151
ISBN 13 9783659442155
Encuadernaci�nTapa blanda
IdiomaIngl�s
N�mero de p�ginas52

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Classification of data mining techniques in intrusion detection

Gaurav Mishra

Publicado por LAP LAMBERT Academic Publishing Aug 2013, 2013

ISBN 10: 3659442151 ISBN 13: 9783659442155

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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

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Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining is the process of gathering, searching, and analyzing a large amount of raw data, as to discover patterns, relationships and behavior of data. There are large numbers of algorithms for classification of data mining. Single algorithm is not efficient for classification of data and recognize their pattern and behavior .There is a key term known as ensemble learning which means Combining two or more classifiers for efficient result. I have used the KDD'99 dataset for the experiment which have 41 features labeled either as normal or as an attack. In this book I have represented how graphical machine learning tool weka can be used for data mining and how ensemble learning can be implemented using weka.I have used three classifiers with the Bagging and Boosting ensemble learning approach which are complementary na�ve bayes and two are rule based classifiers, part and jrip. My experiment shows that bagging improves the efficiency of the rule based classifiers as well as of na�ve Bayes. However, the rule based classifiers become more efficient with bagging and boosting techniques. 52 pp. Englisch. N� de ref. del art�culo: 9783659442155

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Classification of data mining techniques in intrusion detection : Classification Techniques in Data Mining

Gaurav Mishra

Publicado por LAP LAMBERT Academic Publishing, 2013

ISBN 10: 3659442151 ISBN 13: 9783659442155

Nuevo Taschenbuch

Impresi�n bajo demanda

Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificaci�n del vendedor: 5 de 5 estrellas

Taschenbuch. Condici�n: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data mining is the process of gathering, searching, and analyzing a large amount of raw data, as to discover patterns, relationships and behavior of data. There are large numbers of algorithms for classification of data mining. Single algorithm is not efficient for classification of data and recognize their pattern and behavior .There is a key term known as ensemble learning which means Combining two or more classifiers for efficient result. I have used the KDD'99 dataset for the experiment which have 41 features labeled either as normal or as an attack. In this book I have represented how graphical machine learning tool weka can be used for data mining and how ensemble learning can be implemented using weka.I have used three classifiers with the Bagging and Boosting ensemble learning approach which are complementary na�ve bayes and two are rule based classifiers, part and jrip. My experiment shows that bagging improves the efficiency of the rule based classifiers as well as of na�ve Bayes. However, the rule based classifiers become more efficient with bagging and boosting techniques. N� de ref. del art�culo: 9783659442155

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