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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Konakalla A. and Veeranki B. (2013), Evolution of Security Attacks and Security Technology, International Journal of Computer Science and Mobile Computing, 2 (11): 270–276.
[2]	Persson A., Persson A., and Boldt M. (2007), Exploring Phishing Attacks and Countermeasures, (September).
[3]	Bendovschi A. (2016), Cyber-Attacks – Trends, Patterns and Security Countermeasures Cyber-attacks – trends, patterns and security countermeasures, 5671 (January). Retrieved from https://doi.org/10.1016/S2212-5671(15)01077-1.
[4]	Kleitman S., Marvin K. H. L. and Kay J. (2018), It’s the deceiver and the receiver: Individual differences in phishing susceptibility and false positives with item profiling, Individual Differences in Phishing Susceptibility and False Positives with Item Profiling, 13 (10): 1–29.
[5]	Martino, A. S., & Perramon, X. (2010). Phishing Secrets: History, Effects, and Countermeasures. International Journal of Network Security, 11 (3), 163–171.
[6]	Gupta, B. B., Arachchilage, N. A. G., & Psannis, K. E. (2018a). Defending against Phishing Attacks: Taxonomy of Methods, Current Issues and Future Directions. Telecommunication Systems, 67, 1–32.
[7]	Martin, A., Anutthamaa, N. B., Sathyavathy, M., Manjari, M., & Francois, S. (2015). A Framework for Predicting Phishing Websites Using Neural Networks, (September 2011).
[8]	Khonji, M., Iraqi, Y., Member, S., & Jones, A. (2013). Phishing Detection: A Literature Survey, (May 2014). Retrieved from https://doi.org/10.1109/SURV.2013.032213.00009.
[9]	Ubing, A. A., Kamilia, S. J. B., Abdullah, A., Jhanjhi, N. Z., & Supramaniam, M. (2019). Phishing Website Detection : An Improved Accuracy through Feature Selection and Ensemble Learning. International Journal of Advanced Computer Science and Applications (IJACSA), 10 (1), 252–257. Retrieved from https://doi.org/10.14569/IJACSA.2019.0100133.
[10]	Zhang, H., Liu, G., Chow, T. W. S., Member, S., Liu, W., & Member, S. (2011). Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach. IEEE TRANSACTIONS ON NEURAL NETWORKS, 22 (10), 1532–1546.
[11]	Chandan, C. J., Chheda, H. P., Gosar, D. M., Shah, H. R., & Bhave, P. U. (2014). A Machine Learning Approach for Detection of Phished Websites Using Neural Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 2 (12), 4205–4209.
[12]	Mohammad, R. M. A., Mccluskey, T. L., & Thabtah, F. (2013). Predicting Phishing Websites using Neural Network trained with Back - Propagation, (January).
[13]	Nanaware, K., Kanade, K., Bhat, M., Patil, R., & Deokar, A. S. (2014). Malicious Website Detection using Visual Cryptography and OTP. International Journal of Current Engineering and Technology, 4 (5), 3310–3313.
[14]	Jain, N. R., Ujwal, K., Apsara, S., Nikhil, P., & Tejashri, D. (2016). Advance Phishing Detection Using Visual Cryptography And One Time Password. International Journal of Advanced Research in Science, Engineering and Technology, 3 (4), 1808–1812.
[15]	Reshma, R. T. (2015). Detecting Phishing Websites Based On Improved Visual Cryptography. International Journal Of Engineering And Computer Science, 4 (8), 14009–14014. Retrieved from https://doi.org/10.18535/ijecs/v4i8.67.
[16]	Nguyen, A. T. L., To, B. L., & Nguyen, H. K. (2015). An Efficient Approach for Phishing Detection Using Neuro-Fuzzy Model. Journal of Automation and Control Engineering, 3 (6), 519–525. Retrieved from https://doi.org/10.12720/joace.3.6.519-525.
[17]	Swetha, B. K. P., & Damodaram, R. (2016). Phishing Detection in Websites Using Neural Networks and Firefly. International Journal Of Engineering And Computer Science, 5 (9), 18197–18204. Retrieved from https://doi.org/10.18535/ijecs/v5i9.
[18]	Sahingoz, O. K., Saide, I., & Bulut, D. (2018). Phishing Detection from URLs by Using Neural Networks, 41–54.
[19]	Mahalakshmi, A., Goud, N. S., & Murthy, G. V. (2018). A Survey on Phishing and It’s Detection Techniques Based on Support Vector Method (SVM) and Software Defined Networking (SDN). International Journal of Engineering and Advanced Technology, 8 (2), 498–503.
[20]	Jain, A., & Richarya, V. (2011). Implementing a Web Browser with Phishing Detection Techniques. World of Computer Science and Information Technology Journal (WCSIT), 1 (7), 289–291.
[21]	Okunoye, O. B., Azeez, N. A., & Ilurimi, F. A. (2017). A Web Enabled Anti-Phishing Solution Using Enhanced Heuristic Based Technique. FUTA Journal of Research in Sciences, 13 (2), 304–321.
[22]	Yasin, A., & Abuhasan, A. (2016). An Intelligent Classification Model for Phishing Email Detection. International Journal of Network Security & Its Applications, 8 (4), 55–72. Retrieved from https://doi.org/10.5121/ijnsa.2016.8405.
[23]	Gowda, M., Adithya, Prasad, G., & Vinay. (2020). Development of anti-phishing browser based on random forest and rule of extraction framework. Cybersecurity, 3 (1), 1–14. Retrieved from https://doi.org/10.1186/s42400-020-00059-1.
[24]	Ratnaparkhi, P. V., & Jambhulkar, S. S. (2020). Framework for Detection and Prevention of Phishing Website Using Machine Learning. JOURNAL OF CRITICAL REVIEWS, 7 (7), 2108–2125.
[25]	Sonowal, G., & Kuppusamy, K. S. (2020). PhiDMA – A phishing detection model with multi-filter approach. Journal of King Saud University - Computer and Information Sciences, 32 (1), 99–112. Retrieved from https://doi.org/10.1016/j.jksuci.2017.07.005.
[26]	Ali, W. (2017). Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection. International Journal of Advanced Computer Science and Applications (IJACSA), 8 (9), 72–78. Retrieved from https://doi.org/10.14569/IJACSA.2017.080910.
[27]	Alyssa A. U., Syukrina K. B. J., Azween A., Jhanjhi N. Z., and Mahadevan S. (2019), Phishing Website Detection: An Improved Accuracy through Feature Selection and Ensemble Learning, International Journal of Advanced Computer Science and Applications (IJACSA), 10 (1), 252–257.
[28]	Muppavarapu, V., Rajendran, A., & Vasudevan, S. K. (2018). Phishing detection using RDF and random forests. International Arab Journal of Information Technology, 15 (5), 817–824.
[29]	Maurya, S., Saini, H. S., & Jain, A. (2019). Browser extension based hybrid anti-phishing framework using feature selection. International Journal of Advanced Computer Science and Applications, 10 (11), 579–588. Retrieved from https://doi.org/10.14569/IJACSA.2019.0101178.
[30]	Shah, B., Dharamshi, K., Patel, M., & Gaikwad, V. (2020). Chrome Extension for Detecting Phishing Websites. International Research Journal of Engineering and Technology (IRJET), 7 (3), 2958–2962.
[31]	Alswailem, A., Alabdullah, B., Alrumayh, N., & Alsedrani, A. (2019). Detecting Phishing Websites Using Machine Learning. 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019, 7 (2), 1–9. Retrieved from https://doi.org/10.1109/CAIS.2019.8769571.
[32]	Kiruthiga, R., & Akila, D. (2019). Phishing Websites Detection Using Machine Learning. International Journal of Recent Technology and Engineering, 8 (2), 111–114. Retrieved from https://doi.org/10.35940/ijrte.B1018.0982S1119.
[33]	Kulkarni, A., Leonard, L., & Brown. (2019). Phishing Websites Detection using Machine Learning. International Journal of Advanced Computer Science and Applications, 10 (7), 8–13.
[34]	Sudha, M., Jaanavi, V. R., Blessy, I. G. S., & Priyadharshini. (2020). A Review on Phishing Website Detection using Machine Learning. Journal Of Critical Reviews, 7 (19), 4847–4853.
[35]	Natadimadja, M. R., Abdurohman, M., & Nuha, H. H. (2020). A Survey on Phishing Website Detection Using Hadoop. Jurnal Informatika Universitas Pamulang, 5 (3), 237–246.

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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