• DocumentCode
    2035043
  • Title

    Phishing website detection fuzzy system modelling

  • Author

    Barraclough, Phoebe ; Sexton, Graham

  • Author_Institution
    Comput. Sci. & Digital Technol., Northumbria Univ., Newcastle upon Tyne, UK
  • fYear
    2015
  • fDate
    28-30 July 2015
  • Firstpage
    1384
  • Lastpage
    1386
  • Abstract
    This study investigates and identifies parameters in a single platform based on fuzzy system and neural network for phishing websites detection. The new approach utilizes Fuzzy systems, neural network with a set of parameters and a data set to detect phishing sites with high accuracy in real-time. A total of 300 data from six sources were used as training and testing sets using 2-fold cross-validation to train and validate the model, which has achieved the best performance (99.6%) compared to other results in the field.
  • Keywords
    Web sites; computer crime; fuzzy systems; neural nets; parameter estimation; unsolicited e-mail; 2-fold cross-validation; Website detection; data set; fuzzy system modelling; neural network; parameters identification; phishing sites; Accuracy; Feature extraction; Fuzzy logic; Fuzzy systems; Testing; Training; Tuning; fuzzy system; parameters; phishing detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Science and Information Conference (SAI), 2015
  • Conference_Location
    London
  • Type

    conf

  • DOI
    10.1109/SAI.2015.7237323
  • Filename
    7237323