ISSN 2394-5125
 

Review Article 


A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini.

Abstract
Fraudulent communication in the internet is an ever growing issue in the cyber world. This article reviews the negative impacts of fraudulent sites referred as Spoofed websites or phishing websites. These spoofed-sites attempts to steal the essential credentials of any individual by means of false websites that appears same as the original website in the cyber space. Any legitimate user in the Internet communication may prompt to use these spoofed-sites by mistyping the web-address. On the other side when an individual attempts to get his site using a browser cache directly instead of typing the site address on own would lead to these type of spoofed web logging. It is severe issue, as it leads to fiscal losses for both industries and individuals. Therefore this article endeavor to investigate the applicability of widely adopted machine learning model for predicting the Spoofed websites. The proposed algorithm is used to identify and characterize the rules and factors required to classify the spoofed websites. Further these classification techniques are used to identify the relationship between rules and factors to correlate them with each other so as to detect the performance, accuracy, number of rules generated and speed. A Divide and conquer approach is applied in this assessment to detect the spoofed websites. The learning models are trained to match up the distrustful website with the matching legal website by using set of features and if the similarity is higher than the predefined threshold-value then it is declared spoofed-website. The assessment conducted on phishing website detection using machine learning revealed Random Forest tree as suitable detecting the spoofed websites to avoid financial loss and mental stress attaining overall prediction accuracy 92.6%.

Key words: Machine learning, random forest, phishing-website detection and prediction accuracy


 
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How to Cite this Article
Pubmed Style

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini. A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING. JCR. 2020; 7(19): 4847-4853. doi:10.31838/jcr.07.19.568


Web Style

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini. A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING. http://www.jcreview.com/?mno=127856 [Access: September 15, 2020]. doi:10.31838/jcr.07.19.568


AMA (American Medical Association) Style

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini. A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING. JCR. 2020; 7(19): 4847-4853. doi:10.31838/jcr.07.19.568



Vancouver/ICMJE Style

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini. A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING. JCR. (2020), [cited September 15, 2020]; 7(19): 4847-4853. doi:10.31838/jcr.07.19.568



Harvard Style

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini (2020) A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING. JCR, 7 (19), 4847-4853. doi:10.31838/jcr.07.19.568



Turabian Style

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini. 2020. A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING. Journal of Critical Reviews, 7 (19), 4847-4853. doi:10.31838/jcr.07.19.568



Chicago Style

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini. "A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING." Journal of Critical Reviews 7 (2020), 4847-4853. doi:10.31838/jcr.07.19.568



MLA (The Modern Language Association) Style

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini. "A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING." Journal of Critical Reviews 7.19 (2020), 4847-4853. Print. doi:10.31838/jcr.07.19.568



APA (American Psychological Association) Style

Sudha M, Jaanavi R V, Blessy Ida Gladys S, Priyadharshini (2020) A REVIEW ON PHISHING WEBSITE DETECTION USING MACHINE LEARNING. Journal of Critical Reviews, 7 (19), 4847-4853. doi:10.31838/jcr.07.19.568