Detecting phishing websites using machine learning technique.

In recent years, advancements in Internet and cloud technologies have led to a significant increase in electronic trading in which consumers make online purchases and transactions. This growth leads to unauthorized access to users' sensitive information and damages the resources of an enterpris...

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Autor principal:	Ashit Kumar Dutta
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/846ed7e260e84f85ad50ddf0eb7b79b1
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id	oai:doaj.org-article:846ed7e260e84f85ad50ddf0eb7b79b1
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spelling	oai:doaj.org-article:846ed7e260e84f85ad50ddf0eb7b79b12021-12-02T20:17:03ZDetecting phishing websites using machine learning technique.1932-620310.1371/journal.pone.0258361https://doaj.org/article/846ed7e260e84f85ad50ddf0eb7b79b12021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258361https://doaj.org/toc/1932-6203In recent years, advancements in Internet and cloud technologies have led to a significant increase in electronic trading in which consumers make online purchases and transactions. This growth leads to unauthorized access to users' sensitive information and damages the resources of an enterprise. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. In terms of website interface and uniform resource locator (URL), most phishing webpages look identical to the actual webpages. Various strategies for detecting phishing websites, such as blacklist, heuristic, Etc., have been suggested. However, due to inefficient security technologies, there is an exponential increase in the number of victims. The anonymous and uncontrollable framework of the Internet is more vulnerable to phishing attacks. Existing research works show that the performance of the phishing detection system is limited. There is a demand for an intelligent technique to protect users from the cyber-attacks. In this study, the author proposed a URL detection technique based on machine learning approaches. A recurrent neural network method is employed to detect phishing URL. Researcher evaluated the proposed method with 7900 malicious and 5800 legitimate sites, respectively. The experiments' outcome shows that the proposed method's performance is better than the recent approaches in malicious URL detection.Ashit Kumar DuttaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258361 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ashit Kumar Dutta Detecting phishing websites using machine learning technique.
description	In recent years, advancements in Internet and cloud technologies have led to a significant increase in electronic trading in which consumers make online purchases and transactions. This growth leads to unauthorized access to users' sensitive information and damages the resources of an enterprise. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. In terms of website interface and uniform resource locator (URL), most phishing webpages look identical to the actual webpages. Various strategies for detecting phishing websites, such as blacklist, heuristic, Etc., have been suggested. However, due to inefficient security technologies, there is an exponential increase in the number of victims. The anonymous and uncontrollable framework of the Internet is more vulnerable to phishing attacks. Existing research works show that the performance of the phishing detection system is limited. There is a demand for an intelligent technique to protect users from the cyber-attacks. In this study, the author proposed a URL detection technique based on machine learning approaches. A recurrent neural network method is employed to detect phishing URL. Researcher evaluated the proposed method with 7900 malicious and 5800 legitimate sites, respectively. The experiments' outcome shows that the proposed method's performance is better than the recent approaches in malicious URL detection.
format	article
author	Ashit Kumar Dutta
author_facet	Ashit Kumar Dutta
author_sort	Ashit Kumar Dutta
title	Detecting phishing websites using machine learning technique.
title_short	Detecting phishing websites using machine learning technique.
title_full	Detecting phishing websites using machine learning technique.
title_fullStr	Detecting phishing websites using machine learning technique.
title_full_unstemmed	Detecting phishing websites using machine learning technique.
title_sort	detecting phishing websites using machine learning technique.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/846ed7e260e84f85ad50ddf0eb7b79b1
work_keys_str_mv	AT ashitkumardutta detectingphishingwebsitesusingmachinelearningtechnique
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Detecting phishing websites using machine learning technique.

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