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A Comparative Study on Various Mechanisms to Detect Phishing Websites

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TLDR
A structured guide to support the current status of the literature that is based on the anti-phishing mechanism, and a comparative study and analysis of various phishing detection mechanisms.
Abstract
 Abstract— Phishing attacks have been a significant threat to the Internet users and business organizations across the globe causing billions of dollars loss. Phishing is a type of internet fraud that uses emails or websites, which is designed to look like from legitimate organizations, to take in users into disclosing their personal or financial information. This information can be used by hostile party for scandalous and criminal purposes, such as identity theft and fraud. The inability of an end user to accurately sense legitimate and fraudulent emails or websites apart results into a successful phishing attack .The current paper focuses on a comparative study and analysis of various phishing detection mechanisms. This survey is a structured guide to support the current status of the literature that is based on the anti-phishing mechanism.

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Citations
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Proceedings ArticleDOI

Heuristic systematic model based guidelines for phishing victims

TL;DR: In this paper, the authors identified factors of phishing victim based on the heuristic systematic model and proposed phishing awareness guidelines, which are expected to educate Internet users to identify phishing tricks and prevent phishing attacks in the future.
References
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Proceedings ArticleDOI

Why phishing works

TL;DR: This paper provides the first empirical evidence about which malicious strategies are successful at deceiving general users by analyzing a large set of captured phishing attacks and developing a set of hypotheses about why these strategies might work.
Proceedings ArticleDOI

The battle against phishing: Dynamic Security Skins

TL;DR: A new scheme is proposed, Dynamic Security Skins, that allows a remote web server to prove its identity in a way that is easy for a human user to verify and hard for an attacker to spoof.
Proceedings ArticleDOI

An Empirical Analysis of Phishing Blacklists

TL;DR: This paper used 191 fresh phish that were less than 30 minutes old to conduct two tests on eight anti-phishing toolbars and found that two tools using heuristics to complement blacklists caught signicantly more phish initially than those using only blacklists.
Proceedings ArticleDOI

Anomaly Based Web Phishing Page Detection

TL;DR: The idea is to examine the anomalies in Web pages, in particular, the discrepancy between a Web site's identity and its structural features and HTTP transactions, which demands neither user expertise nor prior knowledge of the Web site.
Proceedings ArticleDOI

Examining the impact of website take-down on phishing

TL;DR: Empirical data on phishing website removal times and the number of visitors that the websites attract are analyzed, and it is concluded that website removal is part of the answer to phishing, but it is not fast enough to completely mitigate the problem.