An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier

Adetokunbo MacGregor John-Otumu; Md Mahmudur Rahman; Christiana Ugochinyere Oko

doi:doi:10.11648/j.ajai.20210502.13

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An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier

Adetokunbo MacGregor John-Otumu, Md Mahmudur Rahman, Christiana Ugochinyere Oko

Published in American Journal of Artificial Intelligence (Volume 5, Issue 2)

Received: 12 October 2021 Accepted: 1 November 2021 Published: 5 November 2021

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Abstract

An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions. The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from phishtank. A unique architectural framework for detecting phishing websites was designed using random forest machine learning classifier based the aim and objectives of the study. The model was trained with 90% (9,900) of the dataset and tested with 10% (1,100) using Python programming language for better efficiency. Microsoft Visual Studio Code, Jupiter Notebook, Anaconda Integrated Development Environment, HTML/CSS and JavaScript was used in developing the frontend of the model for easy integration into existing web browsers. The proposed model was also modeled using use-case and sequence diagrams to test its internal functionalities. The result revealed that the proposed model had an accuracy of 0.96, error rate of 0.04, precision of 0.97, recall value of 0.99 and f1-score of 0.98 which far outperform other models developed based on literatures. Future recommendations should focus on improved security features, more phishing adaptive learning properties, and so on, so that it can be reasonably applied to other web browsers in accurately detecting real-world phishing situations using advanced algorithms such as hybridized machine learning and deep learning techniques.

Published in	American Journal of Artificial Intelligence (Volume 5, Issue 2)
DOI	10.11648/j.ajai.20210502.13
Page(s)	66-75
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Phishing, Machine Learning, Random Forest, Web Browsers, Web Sites

References

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APA Style

Adetokunbo MacGregor John-Otumu, Md Mahmudur Rahman, Christiana Ugochinyere Oko. (2021). An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier. American Journal of Artificial Intelligence, 5(2), 66-75. https://doi.org/10.11648/j.ajai.20210502.13

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ACS Style

Adetokunbo MacGregor John-Otumu; Md Mahmudur Rahman; Christiana Ugochinyere Oko. An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier. Am. J. Artif. Intell. 2021, 5(2), 66-75. doi: 10.11648/j.ajai.20210502.13

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AMA Style

Adetokunbo MacGregor John-Otumu, Md Mahmudur Rahman, Christiana Ugochinyere Oko. An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier. Am J Artif Intell. 2021;5(2):66-75. doi: 10.11648/j.ajai.20210502.13

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@article{10.11648/j.ajai.20210502.13,
  author = {Adetokunbo MacGregor John-Otumu and Md Mahmudur Rahman and Christiana Ugochinyere Oko},
  title = {An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier},
  journal = {American Journal of Artificial Intelligence},
  volume = {5},
  number = {2},
  pages = {66-75},
  doi = {10.11648/j.ajai.20210502.13},
  url = {https://doi.org/10.11648/j.ajai.20210502.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20210502.13},
  abstract = {An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions. The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from phishtank. A unique architectural framework for detecting phishing websites was designed using random forest machine learning classifier based the aim and objectives of the study. The model was trained with 90% (9,900) of the dataset and tested with 10% (1,100) using Python programming language for better efficiency. Microsoft Visual Studio Code, Jupiter Notebook, Anaconda Integrated Development Environment, HTML/CSS and JavaScript was used in developing the frontend of the model for easy integration into existing web browsers. The proposed model was also modeled using use-case and sequence diagrams to test its internal functionalities. The result revealed that the proposed model had an accuracy of 0.96, error rate of 0.04, precision of 0.97, recall value of 0.99 and f1-score of 0.98 which far outperform other models developed based on literatures. Future recommendations should focus on improved security features, more phishing adaptive learning properties, and so on, so that it can be reasonably applied to other web browsers in accurately detecting real-world phishing situations using advanced algorithms such as hybridized machine learning and deep learning techniques.},
 year = {2021}
}

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TY  - JOUR
T1  - An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier
AU  - Adetokunbo MacGregor John-Otumu
AU  - Md Mahmudur Rahman
AU  - Christiana Ugochinyere Oko
Y1  - 2021/11/05
PY  - 2021
N1  - https://doi.org/10.11648/j.ajai.20210502.13
DO  - 10.11648/j.ajai.20210502.13
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 66
EP  - 75
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20210502.13
AB  - An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions. The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from phishtank. A unique architectural framework for detecting phishing websites was designed using random forest machine learning classifier based the aim and objectives of the study. The model was trained with 90% (9,900) of the dataset and tested with 10% (1,100) using Python programming language for better efficiency. Microsoft Visual Studio Code, Jupiter Notebook, Anaconda Integrated Development Environment, HTML/CSS and JavaScript was used in developing the frontend of the model for easy integration into existing web browsers. The proposed model was also modeled using use-case and sequence diagrams to test its internal functionalities. The result revealed that the proposed model had an accuracy of 0.96, error rate of 0.04, precision of 0.97, recall value of 0.99 and f1-score of 0.98 which far outperform other models developed based on literatures. Future recommendations should focus on improved security features, more phishing adaptive learning properties, and so on, so that it can be reasonably applied to other web browsers in accurately detecting real-world phishing situations using advanced algorithms such as hybridized machine learning and deep learning techniques.
VL  - 5
IS  - 2
ER  -

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Author Information

Adetokunbo MacGregor John-Otumu

Department of Computer Science, Morgan State University, Baltimore, USA
Md Mahmudur Rahman

Department of Computer Science, Morgan State University, Baltimore, USA
Christiana Ugochinyere Oko

Department of Information Technology, Federal University of Technology, Owerri, Nigeria

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APA Style

Adetokunbo MacGregor John-Otumu, Md Mahmudur Rahman, Christiana Ugochinyere Oko. (2021). An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier. American Journal of Artificial Intelligence, 5(2), 66-75. https://doi.org/10.11648/j.ajai.20210502.13

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ACS Style

Adetokunbo MacGregor John-Otumu; Md Mahmudur Rahman; Christiana Ugochinyere Oko. An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier. Am. J. Artif. Intell. 2021, 5(2), 66-75. doi: 10.11648/j.ajai.20210502.13

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AMA Style

Adetokunbo MacGregor John-Otumu, Md Mahmudur Rahman, Christiana Ugochinyere Oko. An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier. Am J Artif Intell. 2021;5(2):66-75. doi: 10.11648/j.ajai.20210502.13

Copy | Download

@article{10.11648/j.ajai.20210502.13,
  author = {Adetokunbo MacGregor John-Otumu and Md Mahmudur Rahman and Christiana Ugochinyere Oko},
  title = {An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier},
  journal = {American Journal of Artificial Intelligence},
  volume = {5},
  number = {2},
  pages = {66-75},
  doi = {10.11648/j.ajai.20210502.13},
  url = {https://doi.org/10.11648/j.ajai.20210502.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20210502.13},
  abstract = {An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions. The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from phishtank. A unique architectural framework for detecting phishing websites was designed using random forest machine learning classifier based the aim and objectives of the study. The model was trained with 90% (9,900) of the dataset and tested with 10% (1,100) using Python programming language for better efficiency. Microsoft Visual Studio Code, Jupiter Notebook, Anaconda Integrated Development Environment, HTML/CSS and JavaScript was used in developing the frontend of the model for easy integration into existing web browsers. The proposed model was also modeled using use-case and sequence diagrams to test its internal functionalities. The result revealed that the proposed model had an accuracy of 0.96, error rate of 0.04, precision of 0.97, recall value of 0.99 and f1-score of 0.98 which far outperform other models developed based on literatures. Future recommendations should focus on improved security features, more phishing adaptive learning properties, and so on, so that it can be reasonably applied to other web browsers in accurately detecting real-world phishing situations using advanced algorithms such as hybridized machine learning and deep learning techniques.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - An Efficient Phishing Website Detection Plugin Service for Existing Web Browsers Using Random Forest Classifier
AU  - Adetokunbo MacGregor John-Otumu
AU  - Md Mahmudur Rahman
AU  - Christiana Ugochinyere Oko
Y1  - 2021/11/05
PY  - 2021
N1  - https://doi.org/10.11648/j.ajai.20210502.13
DO  - 10.11648/j.ajai.20210502.13
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 66
EP  - 75
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20210502.13
AB  - An efficient phishing website detection plugin service was developed using machine learning technique based on the prevalent phishing threat while using existing web browsers in critical online transactions. The study gathered useful information from 27 published articles and dataset consisting of 11,000 data points with 30 features downloaded from phishtank. A unique architectural framework for detecting phishing websites was designed using random forest machine learning classifier based the aim and objectives of the study. The model was trained with 90% (9,900) of the dataset and tested with 10% (1,100) using Python programming language for better efficiency. Microsoft Visual Studio Code, Jupiter Notebook, Anaconda Integrated Development Environment, HTML/CSS and JavaScript was used in developing the frontend of the model for easy integration into existing web browsers. The proposed model was also modeled using use-case and sequence diagrams to test its internal functionalities. The result revealed that the proposed model had an accuracy of 0.96, error rate of 0.04, precision of 0.97, recall value of 0.99 and f1-score of 0.98 which far outperform other models developed based on literatures. Future recommendations should focus on improved security features, more phishing adaptive learning properties, and so on, so that it can be reasonably applied to other web browsers in accurately detecting real-world phishing situations using advanced algorithms such as hybridized machine learning and deep learning techniques.
VL  - 5
IS  - 2
ER  -

Copy | Download