Evaluation of Phishing Techniques Based on Machine Learning

Evaluation of Phishing Techniques Based on Machine Learning
Author(s):
Afreen Begum, Afroze Ansari
Keywords:

Abstract
Because of the large number of online transactions that occur each day, spoofing destinations is a big concern for online security challenges. The goal of this project is to create an overview of spoofing. A social attack and its identification, as well as raising awareness among customers who are unaware of this serious assault, as many of them are still caught in the trap. A huge majority of the clients are unaware of the problem, and they unintentionally populate several structures that have a Spoofing site that is hidden. This leads to the disclosure of sensitive information about the person in question. This paper also provides a brief overview of a few AI algorithms for predicting Spoofing locations, including Neural Network and Random Forest calculations. On January 2, 1996-97, the term "spoofing" was first used in the Usenet newsgroup AO-Hell to describe a group of programmers stealing client certifications on Usenet (AOL). Spoofing assaults have risen in scale and complexity since then, causing huge monetary and reputational harm to web-based clients. Spoofing is a type of social engineering attack that takes advantage of a vulnerability in the client's system.
Article Details
Unique Paper ID: 153289 Publication Volume & Issue: Volume 8, Issue 6 Page(s): 346 - 350
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