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

Attribute-based feature selection

Cyber theft

Fraudulent website detection

Machine learning algorithms

Data analysis

Author

Mustafa Aydin

Bankacilik Düzenleme ve Denetleme Kurumu

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,

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

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

Resilient Information and Control Systems (RICS)

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

Subject Categories

Other Computer and Information Science

Signal Processing

Computer Science

DOI

10.1109/CCWC47524.2020.9031125

More information

Latest update

9/30/2020