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- W4312640282 abstract "Cyber security is becoming a crucial and indispensable component of the modern era wherein it can be estimated that there will be over 800 million utilizers of web services by the end of the year 2022. Therefore, a need for security solutions to safeguard the general population from phishing scams is of paramount importance as it not only has repercussions on monetary components but also has dire consequences on the psychological well-being of the general population, making them fearful of exploring and utilizing the World Wide Web which is the sole cause to get rid of the probable downsides by formulating reliably methodical frameworks. The sole intent of this endeavour is to target and scrutinize the recurrent idiosyncrasy displayed by several phishing web pages and develop a framework to ascertain such sites on the Internet. The proposed approach to solve the above-mentioned problem is the ensemble model of decision trees having max depth 18, and this ensemble model is created by using K-Fold cross-validation techniques where K models were created, and the max vote classifier made by an ensemble of all of them. 10 models were created, and accuracy for each model ranged from 93.12% to 98.28%. The above method was researched, and it was derived that it is implementable by deploying a web application wherein the utilizer can type the site URL and using the same, the end project would be able to generate figures for several criteria basis of which the developed model had been trained, which would be able to give an analysis to ascertain whether or not a website is fraudulent(Phishing) or legitimate." @default.
- W4312640282 created "2023-01-05" @default.
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- W4312640282 date "2022-01-01" @default.
- W4312640282 modified "2023-09-30" @default.
- W4312640282 title "Phishing Site Detection Using Artificial Intelligence" @default.
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- W4312640282 doi "https://doi.org/10.1007/978-981-19-5037-7_48" @default.
- W4312640282 hasPublicationYear "2022" @default.
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