Enhancing the SVDD accuracy in Intrusion Detection Systems by removing external voids

Conference paper

Kenaza, T., Bennaceur, K., Labed, A. and Aiash, M. 2016. Enhancing the SVDD accuracy in Intrusion Detection Systems by removing external voids. 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-16). Tianjin, China 23 - 25 Aug 2016 Institute of Electrical and Electronics Engineers (IEEE). pp. 1765-1770 https://doi.org/10.1109/TrustCom.2016.0271
TypeConference paper
TitleEnhancing the SVDD accuracy in Intrusion Detection Systems by removing external voids
AuthorsKenaza, T., Bennaceur, K., Labed, A. and Aiash, M.
Abstract

This work aims to improve the accuracy of the SVDD-based Intrusion Detection Systems. In this study we are interested by approaches using only one-class classification, namely the class of normal user sessions. Sessions are modeled by vectors of points in a finite features space. The goal of using the SVDD in anomaly detection is to find the hypersphere with a minimal volume that encloses the entire scatter of points (i.e. the normal sessions). This paper discusses the general case where the shape of the scatter is arbitrary. In this case some voids can occur between the scatter and the boundary of the hypersphere, and mainly cause a distortion of the data description that reduces the accuracy of the detection. The objective of this work is to study and highlight the best techniques that help removing voids and thus improving the accuracy of the SVDD. Experimental results show that choosing the appropriate techniques and parameters can significantly improve the accuracy of the SVDD.

Conference15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-16)
Page range1765-1770
ISSN2324-9013
ISBN
Hardcover9781509032051
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication dates
Online09 Feb 2017
Print26 Aug 2016
Publication process dates
Deposited07 Jun 2017
Accepted20 Jun 2016
Output statusPublished
Accepted author manuscript
Copyright Statement

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Additional information

T. Kenaza, K. Bennaceur, A. Labed and M. Aiash, "Enhancing the SVDD Accuracy in Intrusion Detection Systems by Removing External Voids," 2016 IEEE Trustcom/BigDataSE/ISPA, Tianjin, 2016, pp. 1765-1770.
doi: 10.1109/TrustCom.2016.0271

Digital Object Identifier (DOI)https://doi.org/10.1109/TrustCom.2016.0271
LanguageEnglish
Book title2016 IEEE Trustcom/BigDataSE/ISPA, Tianjin, 2016
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