Affiliations: LRS Laboratory, Faculty of Technology, Department of Computer Science, Badji Mokhtar-Annaba University, Annaba, Algeria
Correspondence:
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Corresponding author: Rania Zaimi, LRS Laboratory, Faculty of Technology, Department of Computer Science, Badji Mokhtar-Annaba University P.O. Box 12, Annaba 23000, Algeria. E-mail: rania.zaimi@univ-annaba.org.
Abstract: Nowadays, with the variety of internet frauds, every web user while browsing the net is vulnerable to being a target of various attacks. The phishing attack is one of the largest and most effective cyber threats; it is a sort of social engineering technique employed by web hackers, with the aim of deceiving users and stealing their credentials for financial gain. The continuous growth and the rising volume of phishing websites have led researchers to propose several anti-phishing solutions to fight against this cyber-attack such as visual similarity-based approaches, list-based approaches, machine learning, heuristics-based techniques … etc, moreover deep learning in recent years has gained increasing interest in several areas, especially in the phishing detection area. In this paper, we propose a deep learning approach to detect phishing websites using convolutional neural networks testing both 1D CNN & 2D CNN with three feature types, URL-based features, content-based features, and third-party services-based features. The experimental results show that 1D CNN is more adequate for phishing detection and it achieves a high accuracy value of 96.76%. Moreover, it reduces the training time compared to other deep learning-based works.
Keywords: Convolutional neural networks, anti-phishing solutions, deep learning, machine learning, cyber security, phishing threat, URL features
