Defending Future Phishing Website Assaults Using Machine Learning

Authors

  • Priya Shelke Vishwakarma Institute of Information Technology, Pune, India
  • Riddhi Mirajkar Vishwakarma Institute of Information Technology, Pune, India
  • Vaishali Joshi KJEI’s Trinity Academy of Engineering, Pune, India
  • Jayashree Jankar Vishwakarma Institute of Information Technology, Pune, India
  • Prajkta Dandavate Vishwakarma Institute of Technology, Pune, India
  • Manisha Dabade Walchand College of Engineering, Sangli, India

Keywords:

MLP, XGBoost, Random Forest, ANN, SVM

Abstract

Phishing has become a more serious issue as a result of a significant increase in internet users. The phishing attacks of today constitute a serious threat to both the online environment and people's daily lives. In these attacks, the attacker poses as a reputable company in order to steal confidential information or the victim's digital identity, such as account login credentials, etc. A phishing website, also known as a faked website, imitates an official website's name and layout in an effort to fool users and steal their personal information. This study will present machine learning and deep learning techniques, and then use all of these algorithms to our dataset to identify fraudulent websites. The approach that works with the highest level of precision and accuracy, is then selected for phishing website detection. We hope that by putting in this effort, future phishing attacks may be better protected against.

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Published

24.03.2024

How to Cite

Shelke, P. ., Mirajkar, R. ., Joshi, V. ., Jankar, J. ., Dandavate, P. ., & Dabade, M. . (2024). Defending Future Phishing Website Assaults Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 658–665. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5297

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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