Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs
Paper in proceeding, 2020

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.

Data analysis

Cyber theft

Machine learning algorithms

Fraudulent website detection

Attribute-based feature selection


Mustafa Aydin

Banking Regulation and Supervision Agency (BRSA)

Middle East Technical University (METU)

Ismail Butun

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Kemal Bicakci

TOBB University of Economics and Technology

Nazife Baykal

Middle East Technical University (METU)

2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020

774-779 9031125

10th Annual Computing and Communication Workshop and Conference, CCWC 2020
Las Vegas, USA,

Resilient Information and Control Systems (RICS)

Swedish Civil Contingencies Agency (2015-828), 2015-09-01 -- 2020-08-31.

Integrated cyber-physical solutions for intelligent distribution grid with high penetration of renewables (UNITED-GRID)

European Commission (EC) (EC/H2020/773717), 2017-11-01 -- 2020-04-30.

Subject Categories

Other Computer and Information Science

Signal Processing

Computer Science



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