Phishing is a cyber-attack on unsuspecting Web users that try to provide confidential information such login, password, social welfare, and credit card data. Attackers impersonate Internet users as a trustworthy or reliable website in order to collect personal details. Phishing is one of the most dangerous Internet crimes and may have huge and negative implications for online business. The problems of phishing assaults are growing considerably in recent years. The phisher constructs a fake or phishing website in a web phishing assault to mislead online users to steal sensitive financial and personal information. In addition to dealing with this difficulty, several standard website detection approaches have been presented. Attackers would typically evade existing URL-based phishing protection systems or page content. This paper explores if a website is authentic or complete and helps to increase website identification accuracy. A selection technique for features is therefore used and incorporated in a majority-voting artificial intelligence approach and is compared with several model classifications, such as a decision tree, a vector support machine, and a navy classifier.
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