Classification of data mining techniques in intrusion detection

Name: Classification of data mining techniques in intrusion detection
Brand: LAP Lambert Academic Publishing
SKU: 9783659442155
Price: 172.39 RON
Availability: InStock

Classification Techniques in Data Mining

Autor: Gaurav Mishra, Gulshan Ahuja

Limbă:

Legare: Carte broșată

Editura: LAP Lambert Academic Publishing

Disponibilitate: În depozitul extern

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Data mining is the process of gathering, searching, and analyzing a large amount of raw data, as to...

Informații despre carte

Autor

Gaurav Mishra, Gulshan Ahuja

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2013

Pagini

EAN

9783659442155

Enbook ID

02070241

Editura

LAP Lambert Academic Publishing

Greutate

Dimensiuni

150 x 220 x 3

Descriere completă

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

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Classification of data mining techniques in intrusion detection

Classification Techniques in Data Mining

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