Using Attribute-based Feature Selection Approaches and Machine Learning Algorithms for Detecting Fraudulent Website URLs

Aydin M., Butun I., Bicakci K., Baykal N.

10th Annual Computing and Communication Workshop and Conference (CCWC), California, Amerika Birleşik Devletleri, 6 - 08 Ocak 2020, ss.774-779 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/ccwc47524.2020.9031125
  • Basıldığı Şehir: California
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.774-779
  • Anahtar Kelimeler: Attribute-based feature selection, Cyber theft, Data analysis, Fraudulent website detection, Machine learning algorithms, INTELLIGENT PHISHING DETECTION
  • İstanbul Teknik Üniversitesi Adresli: Hayır

Özet

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 Naive 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, Naive Bayes performed poor and is the least recommended method among all.