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2000
Volume 18, Issue 2
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Introduction

Users are accessing websites for many purposes, such as obtaining information about a particular topic, buying items, accessing their accounts, . Cybercriminals use phishing websites to attain the sensitive information of the users, like usernames and passwords, credit card details, . Detecting phishing websites helps in protecting the information and the money of people. Machine learning algorithms can be applied to detect phishing websites.

Methods

In this paper, a model based on various machine learning algorithms is developed to detect phishing websites. The machine learning algorithms used in this model are Decision Tree, Random Forest, Extra Trees, K-Nearest Neighbors, Multilayer Perceptron and Support Vector Machine. The dataset of phishing websites is taken from the Kaggle website. The algorithms mentioned above of the developed model are compared together to identify which algorithm has better classification results.

Results

The extra trees algorithm offers the best results for accuracy, precision, and F1-Score. This paper also compares the developed model with a previous model that uses the same dataset and relies upon decision tree, random forest, and support vector machine to determine which model has better classification report results. The developed model, depending on the Decision Tree and SVM, offers better classification results than those of the previous models. The developed model is compared with another preceding model relying upon Decision Tree and Random Forest algorithms to determine which model generates better results for accuracy, precision, recall/sensitivity, and F1-Score.

Conclusion

The developed model, depending on the Decision Tree, presents better results for accuracy, recall, and F1-Score than the results of accuracy, sensitivity, and F1-Score for the preceding model based on the Decision Tree.

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/content/journals/rascs/10.2174/0126662558323858240612064259
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A Developed Model Based on Machine Learning Algorithms for Phishing Website Detection
Recent Advances in Computer Science and Communications 18, E140624231032 (2025); https://doi.org/10.2174/0126662558323858240612064259
/content/journals/rascs/10.2174/0126662558323858240612064259
/content/journals/rascs/10.2174/0126662558323858240612064259
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  • Article Type:     Research Article
Keyword(s): classification metrics; classification report; cybersecurity; machine learning; phishing detection; Phishing websites

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