Detecting Phishing Sites -- An Overview

• URL: http://arxiv.org/abs/2103.12739v1
• Date: Tue, 23 Mar 2021 19:16:03 GMT
• Title: Detecting Phishing Sites -- An Overview
• Authors: P.Kalaharsha (1, 2), B.M.Mehtre (1) ((1) Center of excellence in cyber security, Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, India, (2) School of Computer Science and Information Sciences (SCIS), University of Hyderabad, Hyderabad, India)
• Abstract summary: Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. To minimize the damage caused by phishing must be detected as early as possible. There are various phishing detection techniques based on white-list, black-list, content-based, URL-based, visual-similarity and machine-learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal in-formation. To minimize the damage caused by phishing must be detected as early as possible. There are various phishing attacks like spear phishing, whaling, vishing, smishing, pharming and so on. There are various phishing detection techniques based on white-list, black-list, content-based, URL-based, visual-similarity and machine-learning. In this paper, we discuss various kinds of phishing attacks, attack vectors and detection techniques for detecting the phishing sites. Performance comparison of 18 different models along with nine different sources of datasets are given. Challenges in phishing detection techniques are also given.

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