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- W3111962011 abstract "Today, everyone is highly dependent on the internet. Everyone performed online shopping and online activities such as online Bank, online booking, online recharge and more on internet. Phishing is a type of website threat and phishing is Illegally on the original website Information such as login id, password and information of credit card. This paper proposed an efficient machine learning based phishing detection technique. Overall, experimental results show that the proposed technique, when integrated with the Support vector machine classifier, has the best performance of accurately distinguishing 95.66% of phishing and appropriate websites using only 22.5% of the innovative functionality. The proposed technique exhibits optimistic results when benchmarking with a range of standard phishing datasets of the “University of California Irvine (UCI)” archive. Therefore, proposed technique is preferred and used for phishing detection based on machine learning." @default.
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- W3111962011 date "2020-11-01" @default.
- W3111962011 modified "2023-10-05" @default.
- W3111962011 title "Phishing Detection Using Machine Learning Technique" @default.
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- W3111962011 doi "https://doi.org/10.1109/smart-tech49988.2020.00026" @default.
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