ANALYZING MACHINE LEARNING APPROACHES FOR PHISHING WEBSITE DETECTION

Abstract

With the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyber world. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus webpages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, ete. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. Phishing attacks are malicious attempts to deceive users into revealing sensitive information, such as login credentials or financial details, by masquerading as legitimate entities. These attacks often involve fraudulent websites or emails designed to trick users into disclosing confidential information