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Search: WFRF:(Baykal Nazife)

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  • Aydin, Mustafa, et al. (author)
  • Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs
  • 2020
  • In: 2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020. ; , s. 774-779
  • Conference paper (peer-reviewed)abstract
    • Phishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Naïve Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm; Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly; SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Naïve Bayes performed poor and is the least recommended method among all.
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  • Result 1-1 of 1
Type of publication
conference paper (1)
Type of content
peer-reviewed (1)
Author/Editor
Aydin, Mustafa (1)
Butun, Ismail, 1981 (1)
Bicakci, Kemal (1)
Baykal, Nazife (1)
University
Chalmers University of Technology (1)
Language
English (1)
Research subject (UKÄ/SCB)
Natural sciences (1)
Engineering and Technology (1)
Year
2020 (1)

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