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A novel cloud intrusion detection system using feature selection and classification
KTH, School of Information and Communication Technology (ICT).
KTH, School of Information and Communication Technology (ICT).
KTH, School of Information and Communication Technology (ICT).
KTH, School of Information and Communication Technology (ICT).
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2015 (English)In: International Journal of Intelligent Information Technologies, ISSN 1548-3657, E-ISSN 1548-3665, Vol. 11, no 4, 1-15 p.Article in journal (Refereed) Published
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Abstract [en]

This paper proposes a new cloud intrusion detection system for detecting the intruders in a traditional hybrid virtualized, cloud environment. The paper introduces an effective feature selection algorithm called Temporal Constraint based on Feature Selection algorithm and also proposes a classification algorithm called hybrid decision tree. This hybrid decision tree has been developed by extending the Enhanced C4.5 algorithm an existing decision tree based classifier. Furthermore, the experiments conducted on the sample Cloud Intrusion Detection Datasets (CIDD) show that the proposed cloud intrusion detection system provides better detection accuracy than the existing work and reduces the false positive rate.

Place, publisher, year, edition, pages
IGI Global, 2015. Vol. 11, no 4, 1-15 p.
Keyword [en]
Cloud intrusion detection datasets (CIDD), Cloud intrusion detection system (CIDS), Decision tree (DT), Enhanced C4.5 decision tree, Feature selection
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-187102DOI: 10.4018/IJIIT.2015100101Scopus ID: 2-s2.0-84954514109OAI: oai:DiVA.org:kth-187102DiVA: diva2:929727
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QC 20160519

Available from: 2016-05-19 Created: 2016-05-17 Last updated: 2016-05-19Bibliographically approved

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Kannan, AnandVenkatesan, Karthik GururajanStagkopoulou, AlexandraLi, Sheng
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CiteExportLink to record
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Citation style
  • apa
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  • nn-NB
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  • Other locale
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Output format
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