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 |
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 |
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