MURAL - Maynooth University Research Archive Library

    Steady State RF Fingerprinting for Identity Verification: One Class Classifier Versus Customized Ensemble

    Kroon, Bernard and Bergin, Susan and Kennedy, Irwin O. and O'Mahony Zamora, Georgina (2010) Steady State RF Fingerprinting for Identity Verification: One Class Classifier Versus Customized Ensemble. In: AICS 2009: Artificial Intelligence and Cognitive Science. Lecture Notes in Computer Science (LNCS) (6206). Springer Verlag, pp. 198-206. ISBN 9783642170799

    Download (128kB) | Preview

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    Mobile phone proliferation and increasing broadband penetration presents the possibility of placing small cellular base stations within homes to act as local access points. This can potentially lead to a very large increase in authentication requests hitting the centralized authentication infrastructure unless access is mediated at a lower protocol level. A study was carried out to examine the effectiveness of using Support Vector Machines to accurately identify if a mobile phone should be allowed access to a local cellular base station using differences imbued upon the signal as it passes through the analogue stages of its radio transmitter. Whilst allowing prohibited transmitters to gain access at the local level is undesirable and costly, denying service to a permitted transmitter is simply unacceptable. Two different learning approaches were employed, the first using One Class Classifiers (OCCs) and the second using customized ensemble classifiers. OCCs were found to perform poorly, with a true positive (TP) rate of only 50% (where TP refers to correctly identifying a permitted transmitter) and a true negative (TN) rate of 98% (where TN refers to correctly identifying a prohibited transmitter). The customized ensemble classifier approach was found to considerably outperform the OCCs with a 97% TP rate and an 80% TN rate.

    Item Type: Book Section
    Keywords: Machine Learning; Classification; Ensemble Classifiers; Support Vector Machines; One Class Classifiers;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8684
    Depositing User: Dr. Susan Bergin
    Date Deposited: 25 Aug 2017 11:09
    Journal or Publication Title: AICS 2009: Artificial Intelligence and Cognitive Science
    Publisher: Springer Verlag
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads