Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3839
Title: Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs
Authors: Aydın, M. Akif
Bütün, İsmail
Bıçakcı, Kemal
Baykal, N.
Keywords: Attribute-based feature selection
cyber theft
data analysis
fraudulent website detection
machine learning algorithms
Issue Date: Jan-2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Aydin, M., Butun, I., Bicakci, K. and Baykal, N. (2020, January). Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs. In 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0774-0779). IEEE.
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. © 2020 IEEE.
URI: https://hdl.handle.net/20.500.11851/3839
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9031125
ISBN: 978-172813783-4
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

SCOPUSTM   
Citations

1
checked on Sep 23, 2022

WEB OF SCIENCETM
Citations

2
checked on Feb 4, 2023

Page view(s)

26
checked on Feb 6, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.