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2018 | OriginalPaper | Chapter

An Anomaly Intrusion Detection System Using C5 Decision Tree Classifier

Authors : Ansam Khraisat, Iqbal Gondal, Peter Vamplew

Published in: Trends and Applications in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

Due to increase in intrusion activities over internet, many intrusion detection systems are proposed to detect abnormal activities, but most of these detection systems suffer a common problem which is producing a high number of alerts and a huge number of false positives. As a result, normal activities could be classified as intrusion activities. This paper examines different data mining techniques that could minimize both the number of false negatives and false positives. C5 classifier’s effectiveness is examined and compared with other classifiers. Results should that false negatives are reduced and intrusion detection has been improved significantly. A consequence of minimizing the false positives has resulted in reduction in the amount of the false alerts as well. In this study, multiple classifiers have been compared with C5 decision tree classifier using NSL_KDD dataset and results have shown that C5 has achieved high accuracy and low false alarms as an intrusion detection system.

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Metadata
Title
An Anomaly Intrusion Detection System Using C5 Decision Tree Classifier
Authors
Ansam Khraisat
Iqbal Gondal
Peter Vamplew
Copyright Year
2018
Book
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-04502-9

Electronic ISBN: 978-3-030-04503-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-04503-6_14

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