Phishing Analysis of Websites Using Classification Techniques

Aksu D., Turgut Z., Ustebay S., Aydin M. A.

1st International Telecommunications Conference (ITelCon), İstanbul, Turkey, 28 - 29 December 2017, vol.504, pp.251-258 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 504
  • Doi Number: 10.1007/978-981-13-0408-8_21
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.251-258


In today's world, where all records are carried into an electronic environment, cyber security represents a very broad scope, with the primary objective of preventing the loss of financial and/or emotional loss of people, institutions, organizations through the security of data in the digital environment. Today, the most common cyber security threat is phishing attacks. With the phishing attack, the attacker aims to capture the data which are very important for the individuals like identification number, social security number, bank account information, and so on. In this study, using deep learning, it was checked whether the web sites are real or not by using neural networks and support vector machine, decision tree and stacked autoencoders as classification methods. As a result of the study, 86% success rate was reached by using stacked autoencoders which are a part of deep learning techniques.