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Basnet, R.B., Sung, A.H. and Liu, Q. (2012) Feature Selection for Improved Phishing Detection. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer, Berlin, Heidelber, 252-261.
https://doi.org/10.1007/978-3-642-31087-4_27

has been cited by the following article:

• TITLE: Artificial Neural Network for Websites Classification with Phishing Characteristics

  AUTHORS: Ricardo Pinto Ferreira, Andréa Martiniano, Domingos Napolitano, Marcio Romero, Dacyr Dante De Oliveira Gatto, Edquel Bueno Prado Farias, Renato José Sassi

  KEYWORDS: Artificial Intelligence, Artificial Neural Network, Pattern Recognition, Phishing Characteristics, Social Engineering

  JOURNAL NAME: Social Networking, Vol.7 No.2, April 27, 2018

  ABSTRACT: Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on the Internet by criminals for online fraud. The Artificial Neural Networks (ANN) are computational models inspired by the structure of the brain and aim to simu-late human behavior, such as learning, association, generalization and ab-straction when subjected to training. In this paper, an ANN Multilayer Per-ceptron (MLP) type was applied for websites classification with phishing cha-racteristics. The results obtained encourage the application of an ANN-MLP in the classification of websites with phishing characteristics.