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Title: :  Detection of Phishing Website Using Machine Learning and Features Extraction
PaperId: :  25616
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 1 2025
DUI:    16.0415/IJARIIE-25616
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Amaefule I.AImo State University Owerri, Imo State Nigeria
Ubochi C.IImo State University Owerri, Imo State Nigeria
Anamelechi F.CImo State University Owerri, Imo State Nigeria

Abstract

Computer security
Phishing, Detection, Machine Learning, Neural Network, Authentication, Identification
Phishing assaults are fast-expanding menace in cyberspace which costs web users and organizations huge financial loss annually. Sensitive information obtained from customers via various social engineering methods is prohibited, including Web sites, pop-up messages, instant messaging, email, and other communication channels can all be utilized to spot phishing attempts. This paper provides a framework that can identify phishing or real URL links. A collection of harmless, junk mail, malware, phishing, and defacement URLs are included in the data set employed for categorization. Additionally, phishing URLs from an open-source platform called "Phish Tank," which offers phishing URLs in various forms like JSON, CSV, and others, are included. Six (6) models of machine learning and deep neural network techniques are used to identify phishing URLs. With a collection of over 10,000 randomly chosen URLs, split into 60% training and 40% testing samples, and comprising up to 23,328 phishing and 4894 valid URLs, the research purpose is the development of online applications that can quickly recognize phishing URLs. The Uniform Resource Locator datasets has been trained and evaluated utilizing feature selections such as HTTPS & JavaScript-based features, domain-based features, address bar-based features in order to differentiate among legitimate and phishing URLs.  The research provided a method for classifying URLs into legitimate and fraudulent URLs. In order to help individuals and organizations spot phishing links and stay one step ahead of the criminal, it would be very beneficial to authenticate each link that is delivered to them in order to verify its credibility.

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IJARIIE Amaefule I.A, Ubochi C.I, and Anamelechi F.C. "Detection of Phishing Website Using Machine Learning and Features Extraction" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 1 2025 Page 164-175
MLA Amaefule I.A, Ubochi C.I, and Anamelechi F.C. "Detection of Phishing Website Using Machine Learning and Features Extraction." International Journal Of Advance Research And Innovative Ideas In Education 11.1(2025) : 164-175.
APA Amaefule I.A, Ubochi C.I, & Anamelechi F.C. (2025). Detection of Phishing Website Using Machine Learning and Features Extraction. International Journal Of Advance Research And Innovative Ideas In Education, 11(1), 164-175.
Chicago Amaefule I.A, Ubochi C.I, and Anamelechi F.C. "Detection of Phishing Website Using Machine Learning and Features Extraction." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 1 (2025) : 164-175.
Oxford Amaefule I.A, Ubochi C.I, and Anamelechi F.C. 'Detection of Phishing Website Using Machine Learning and Features Extraction', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 1, 2025, p. 164-175. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Detection_of_Phishing_Website_Using_Machine_Learning_and_Features_Extraction_ijariie25616.pdf (Accessed : ).
Harvard Amaefule I.A, Ubochi C.I, and Anamelechi F.C. (2025) 'Detection of Phishing Website Using Machine Learning and Features Extraction', International Journal Of Advance Research And Innovative Ideas In Education, 11(1), pp. 164-175IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Detection_of_Phishing_Website_Using_Machine_Learning_and_Features_Extraction_ijariie25616.pdf (Accessed : )
IEEE Amaefule I.A, Ubochi C.I, and Anamelechi F.C, "Detection of Phishing Website Using Machine Learning and Features Extraction," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 1, pp. 164-175, Jan-Feb 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Detection_of_Phishing_Website_Using_Machine_Learning_and_Features_Extraction_ijariie25616.pdf [Accessed : ].
Turabian Amaefule I.A, Ubochi C.I, and Anamelechi F.C. "Detection of Phishing Website Using Machine Learning and Features Extraction." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 1 ().
Vancouver Amaefule I.A, Ubochi C.I, and Anamelechi F.C. Detection of Phishing Website Using Machine Learning and Features Extraction. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(1) : 164-175. Available from: https://ijariie.com/AdminUploadPdf/Detection_of_Phishing_Website_Using_Machine_Learning_and_Features_Extraction_ijariie25616.pdf
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