NDLI: Comprehensive Method for Detecting Phishing EmailsUsing Correlation-based Analysis and User Participation

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Comprehensive Method for Detecting Phishing EmailsUsing Correlation-based Analysis and User Participation

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Comprehensive Method for Detecting Phishing EmailsUsing Correlation-based Analysis and User Participation

Content Provider	ACM Digital Library
Author	Verma, Rakesh El Aassal, Ayman
Abstract	Phishing email has become a popular solution among attackers to steal all kinds of data from people and easily breach organizations' security. Hackers use multiple techniques and tricks to raise the chances of success of their attacks, like using information found on social networking websites to tailor their emails to the target's interests, or targeting employees of an organization who probably can't spot a phishing email or malicious websites and avoid sending emails to IT people or employees from Security department. In this paper we focus on analyzing the coherence of information contained in the different parts of the email: Header, Body, and URLs. After analyzing multiple phishing emails we discovered that there is always incoherence between these different parts. We created a comprehensive method which uses a set of rules that correlates the information collected from analyzing the header, body and URLs of the email and can even include the user in the detection process. We take into account that there is no such thing called perfection, so even if an email is classified as legitimate, our system will still send a warning to the user if the email is suspicious enough. This way even if a phishing email manages to escape our system, the user can still be protected.
Starting Page	155
Ending Page	157
Page Count	3
File Format	PDF
ISBN	9781450345231
DOI	10.1145/3029806.3029842
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-03-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Correlation Phishing email Email format Headers Comprehensive method Body Url
Content Type	Text
Resource Type	Article

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