Machine Learning Approaches for Phishing Detection: A Comparative Study

Year : 2024 | Volume :02 | Issue : 01 | Page : 35-45
By

Prerana Talekar,

Akanksha Pawar,

Rutuja Yadav,

Nikita Dhamal,

  1. Student,, Shri Chhatrapati Shivajiraje College of Engineering, Dhangwadi,, Maharashtra, India
  2. Student,, Shri Chhatrapati Shivajiraje College of Engineering, Dhangwadi,, Maharashtra, India
  3. Student,, Shri Chhatrapati Shivajiraje College of Engineering, Dhangwadi,, Maharashtra, India
  4. Student,, Shri Chhatrapati Shivajiraje College of Engineering, Dhangwadi,, Maharashtra, India

Abstract

‘]

These days, everyone has an internet addiction. All of us have used the internet for banking, booking, recharging, and buying. Phishing is a type of website threat that exists online. On the original website, phishing is an attempt to illegally obtain information such as login ID, password and credit card information. In this research, we proposed an efficient phishing detection system based on machine learning. Overall, the experimental findings demonstrated that the suggested method performs best when used in tandem with support vector machine classifiers, detecting 95.66% of phishing attempts and matching webpages with just 22.5% of novel functionality. When the suggested method is contrasted with many popular phishing datasets from UCI’s repository, encouraging results are observed. Therefore, for machine learning-based phishing detection, the suggested technique is the one that is employed and chosen.

Keywords: Phishing, web, machine learning, principal component analysis, support vector machine, random forest, decision tree, naïve bayes

[This article belongs to International Journal of Satellite Remote Sensing (ijsrs)]

How to cite this article:
Prerana Talekar, Akanksha Pawar, Rutuja Yadav, Nikita Dhamal. Machine Learning Approaches for Phishing Detection: A Comparative Study. International Journal of Satellite Remote Sensing. 2024; 02(01):35-45.
How to cite this URL:
Prerana Talekar, Akanksha Pawar, Rutuja Yadav, Nikita Dhamal. Machine Learning Approaches for Phishing Detection: A Comparative Study. International Journal of Satellite Remote Sensing. 2024; 02(01):35-45. Available from: https://journals.stmjournals.com/ijsrs/article=2024/view=170778

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Regular Issue Subscription Original Research
Volume 02
Issue 01
Received May 23, 2024
Accepted June 10, 2024
Published September 7, 2024
Views: 38

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