- Title
- An anomaly intrusion detection system using C5 decision tree classifier
- Creator
- Khraisat, Ansam; Gondal, Iqbal; Vamplew, Peter
- Date
- 2018
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/168118
- Identifier
- vital:13785
- Identifier
-
https://doi.org/10.1007/978-3-030-04503-6_14
- Identifier
- ISBN:03029743 (ISSN); 9783030045029 (ISBN)
- 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.; Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Publisher
- Springer Verlag
- Relation
- 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018; Melbourne, Australia; 3rd June 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11154 LNAI, p. 149-155
- Rights
- Copyright © Springer Nature Switzerland AG 2018.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Anomaly detection; Intrusion detection system; Malware; NSL_KDD
- Reviewed
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