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Tytuł:
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Detecting phishing websites using machine learning technique.
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Autorzy:
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Dutta AK; Department of Computer Science and Information System, College of Applied Sciences, Almaarefa University, Riyadh, Saudi Arabia.
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Źródło:
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PloS one [PLoS One] 2021 Oct 11; Vol. 16 (10), pp. e0258361. Date of Electronic Publication: 2021 Oct 11 (Print Publication: 2021).
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: San Francisco, CA : Public Library of Science
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MeSH Terms:
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Computer Security*
Internet*
Machine Learning*
Algorithms ; Databases as Topic ; Learning Curve
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Entry Date(s):
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Date Created: 20211011 Date Completed: 20211125 Latest Revision: 20211125
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Update Code:
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20240104
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PubMed Central ID:
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PMC8504731
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DOI:
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10.1371/journal.pone.0258361
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PMID:
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34634081
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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.
Competing Interests: No conflict of interest.