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Paper Title : An Efficient Model for Detecting Uniform Resource Locator (URL) Phishing using Machine Learning Techniques

ISSN : 2394-2231
Year of Publication : 2021

10.29126/23942231/IJCT-v8i3p10
Authors: Nkue Dumka,Dr. Daniel Matthias,Dr. E.O Bennett

MLA Style: Nkue Dumka,Dr. Daniel Matthias,Dr. E.O Bennett " An Efficient Model for Detecting Uniform Resource Locator (URL) Phishing using Machine Learning Techniques " Volume 8 - Issue 3 May-June , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Nkue Dumka,Dr. Daniel Matthias,Dr. E.O Bennett " An Efficient Model for Detecting Uniform Resource Locator (URL) Phishing using Machine Learning Techniques " Volume 8 - Issue 3 May-June , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
One of the major challenges that researchers face in the field of e-fraud detection is a rapid shift in fraud trends. There are three key forms of e-fraud, namely: spam email, phishing and infiltration of the network. As many organizations and individuals worldwide have become victims, phishing remains a major threat to global security and the economy. As technology progresses, it is a lucrative trade that will keep blooming. The rapid pace of development of new websites for phishing and distributed phishing attacks has made it difficult to keep blacklists up to date. This paper concentrated on developing an efficient model for detecting phishing URL using machine learning technique. Link Guard algorithm was used to extract real and visual links from the Domain Name System (DNS) and compared the actual link and the visual link to check if the links are the same. Support Vector Machine has been used to train and classify URL into genuine and phishing URL. The system was implemented in Python programming language. Experiments were conducted using two publicly accessible website databases to test the efficiency of the identification of phishing websites. 3200 websites samples were used, 2127 genuine websites and 1036 websites were phishing. The LinkGuard and SVM yielded an accuracy of 98.8%.

Reference
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Keywords
—Efficient Model, Phishing URL, Machine Learning Techniques

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