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

Language: English

Published by LAP LAMBERT Academic Publishing, 2013

3659442151 / 9783659442155

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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 naïve 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 naïve Bayes. However, the rule based classifiers become more efficient with bagging and boosting techniques.

Seller Inventory # 9783659442155

Title
Classification of data mining techniques in intrusion detection : Classification Techniques in Data Mining
Author
Gaurav Mishra
Publisher
LAP LAMBERT Academic Publishing
Publication year
2013
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3659442151
ISBN 13
9783659442155
Item weight
96 grams
Dimensions
220x150x4 mm

AHA-BUCH GmbH

Einbeck, Germany

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