Unsupervised feature selection method for intrusion detection system

Mohammed A. Ambusaidi, Xiangjian He, Priyadarsi Nanda

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

This paper considers the feature selection problem for data classification in the absence of data labels. It first proposes an unsupervised feature selection algorithm, which is an enhancement over the Laplacian score method, named an Extended Laplacian score, EL in short. Specifically, two main phases are involved in EL to complete the selection procedures. In the first phase, the Laplacian score algorithm is applied to select the features that have the best locality preserving power. In the second phase, EL proposes a Redundancy Penalization (RP) technique based on mutual information to eliminate the redundancy among the selected features. This technique is an enhancement over Battiti's MIFS. It does not require a user-defined parameter such as beta to complete the selection processes of the candidate feature set as it is required in MIFS. After tackling the feature selection problem, the final selected subset is then used to build an Intrusion Detection System. The effectiveness and the feasibility of the proposed detection system are evaluated using three well-known intrusion detection datasets: KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results confirm that our feature selection approach performs better than the Laplacian score method in terms of classification accuracy.

Original languageEnglish
Title of host publicationProceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages295-301
Number of pages7
ISBN (Electronic)9781467379519
DOIs
Publication statusPublished - 2 Dec 2015
Externally publishedYes
Event14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015 - Helsinki, Finland
Duration: 20 Aug 201522 Aug 2015

Publication series

NameProceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015
Volume1

Conference

Conference14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015
Country/TerritoryFinland
CityHelsinki
Period20/08/1522/08/15

Keywords

  • Intrusion detection system
  • Mutual information
  • Supervised feature selection
  • Unsupervised feature selection

ASJC Scopus subject areas

  • Computer Networks and Communications

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