Intrusion Detection Methods Using an Ensemble of Decision Trees

Intrusion Detection Methods Using an Ensemble of Decision Trees

AngličtinaMäkká väzbaTlač na objednávku
Kishor Kumar, Gulla
LAP Lambert Academic Publishing
EAN: 9786202008396
Tlač na objednávku
Predpokladané dodanie v utorok, 7. mája 2024
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Podrobné informácie

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.
EAN 9786202008396
ISBN 6202008393
Typ produktu Mäkká väzba
Vydavateľ LAP Lambert Academic Publishing
Stránky 140
Jazyk English
Rozmery 220 x 150
Autori Ananda Rao, A; Kishor Kumar, Gulla; Pulabaigari, Viswanath