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Genetic algorithm based feature selection algorithm for effective intrusion detection in cloud networks
KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS.
KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS. (Cos Radiosystem Lab - Rs-Lab)ORCID iD: 0000-0002-6066-746X
2012 (English)In: Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012, IEEE , 2012, 416-423 p.Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing is expected to provide on-demand, agile, and elastic services. Cloud networking extends cloud computing by providing virtualized networking functionalities and allows various optimizations, for example to reduce latency while increasing flexibility in the placement, movement, and interconnection of these virtual resources. However, this approach introduces new security challenges. In this paper, we propose a new intrusion detection model in which we combine a newly proposed genetic based feature selection algorithm and an existing Fuzzy Support Vector Machines (SVM) for effective classification as a solution. The feature selection reduces the number of features by removing unimportant features, hence reducing runtime. Moreover, when the Fuzzy SVM classifier is used with the reduced feature set, it improves the detection accuracy. Experimental results of the proposed combination of feature selection and classification model detects anomalies with a low false alarm rate and a high detection rate when tested with the KDD Cup 99 data set.

Place, publisher, year, edition, pages
IEEE , 2012. 416-423 p.
Keyword [en]
Fuzzy support vector machine (FSVM), Genetic algorithm (GA), Intrusion detection system (IDS), Tenfold cross validation
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-118397DOI: 10.1109/ICDMW.2012.56ISI: 000320946500055Scopus ID: 2-s2.0-84873161461ISBN: 978-076954925-5 (print)OAI: oai:DiVA.org:kth-118397DiVA: diva2:606323
Conference
12th IEEE International Conference on Data Mining Workshops, ICDMW 2012, 10 December 2012 through 10 December 2012, Brussels
Note

QC 20130219

Available from: 2013-02-19 Created: 2013-02-18 Last updated: 2013-09-09Bibliographically approved

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Maguire Jr., Gerald Q.

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf