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  Published Paper Details:

  Paper Title

PHISHING WEBSITE DETECTION AND PREVENTION BASED ON LOGISTIC REGRESSION

  Authors

  Priyanka Gupta,  Amit Mahajan

  Keywords

Phishing, Machine Learning, Logistic Regression, and website Links.

  Abstract


Phishing is becoming one of the most serious and rapidly growing threats, because phishing attackers obtain data that was entered by the client and use it without the customer's knowledge. In today's world, personal information is more valuable than money, hence phishing website attackers are focused on the user's personal information and exploiting it when the user submits personal information into phishing websites. As a result, the fundamental goal of these efforts is to prevent phishing attackers from misusing personal information. To solve this problem, a machine learning approach called logistic regression is utilized with a large dataset. This dataset is used to train the algorithm, which aids in the detection of new web connections that are fake. Attackers masquerade their website as legitimate in order to obtain data from users. They entice visitors to visit a website in order to obtain the personal information required. It is critical to determine whether the provided link is good or a phishing link before attempting to access such websites. We can protect ourselves from intruders and keep our data safe by verifying the link.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2203329

  Paper ID - 217050

  Page Number(s) - c826-c830

  Pubished in - Volume 10 | Issue 3 | March 2022

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Priyanka Gupta,  Amit Mahajan,   "PHISHING WEBSITE DETECTION AND PREVENTION BASED ON LOGISTIC REGRESSION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 3, pp.c826-c830, March 2022, Available at :http://www.ijcrt.org/papers/IJCRT2203329.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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