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Detection of Phishing Web Sites Based On Feature Classification and Extreme Learning Machine

EasyChair Preprint no. 2425

5 pagesDate: January 20, 2020


ABSTRACT: Phishing sites which expects to take the victims confidential data by diverting them to surf a fake website page that resembles a honest to goodness one is another type of criminal acts through the internet and its one of the especially concerns toward numerous areas including e-managing an account and retailing. Phishing site detection is truly an unpredictable and element issue including numerous components and criteria that are not stable. We proposed an intelligent model for detecting phishing web pages based on Extreme Learning Machine. Types of web pages are different in terms of their features. Hence, we must use a specific web page features set to prevent phishing attacks. We proposed a model based on machine learning techniques to detect phishing web pages. We have suggested some new rules to have efficient feature classification. The model has 30 inputs and 1 output. In this application, the 10-fold cross-validation test has been performed. The average classification accuracy measured as 95.05%.

Keyphrases: Extreme Learning Machine, Feature classification, Phishing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Pankaj Kumar Kandi and Pankaj Agarkar},
  title = {Detection of Phishing Web Sites Based On Feature Classification and Extreme Learning Machine},
  howpublished = {EasyChair Preprint no. 2425},

  year = {EasyChair, 2020}}
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