Intrusion Detection Methods Using an Ensemble of Decision Trees
Language: English
Published by LAP LAMBERT Academic Publishing, 2017
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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£ 49.39
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Intrusion detection corresponds to a set of techniques that are used to find attacks which damages the computers and network infrastructures. Intrusion detection is a classification problem. Therefore, data mining techniques can be used to classify a given network connection to either a normal connection or an anomaly connection. To do this, various classification models can be used. Among all, decision tree classifiers have become very popular because of its simplicity, interpretability and its performance. However, decision tree classifiers are known to have high variance. Therefore, it is said to be an unstable classifier. Along with these, the conventional decision tree classifier does not perform well when noise, vagueness and uncertainty present in the data. However, to resolve the above issues this book proposes to use an ensemble of decision tree classifiers. To show the effectiveness of the proposed methods, various intrusion detection data sets along with standard data sets are used. …
Seller Inventory # 9786202008396
- Title
- Intrusion Detection Methods Using an Ensemble of Decision Trees
- Author
- Gulla Kishor Kumar
- Publisher
- LAP LAMBERT Academic Publishing
- Publication year
- 2017
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 6202008393
- ISBN 13
- 9786202008396
- Item weight
- 227 grams
- Dimensions
- 220x150x9 mm
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