Machine Learning-Based Phishing Detection (original) (raw)

Abstract

Millions of users have been successfully connected globally by the internet today, and as a result, users' reliance on this platform for data browsing, online transactions, and information downloads has grown. Cybersecurity is a term for a collection of technologies and procedures used to safeguard software and hardware against intrusion, harm, and attacks. DoS attacks, Man-inthe-Middle attacks, Phishing attacks, SQL Injection attacks, etc. are some of the most often seen cybersecurity threats. There has been an uptick in consumers losing access to their very sensitive and private information over the past few years. These days, fraudsters utilise such methods to trick their victims in an effort to steal personal information including their username, password, bank account information, and credit card information. Attacks against users are frequently delivered via spoofing emails, illegal websites, malware, etc. To handle complicated and massive amounts of data, a structured automated technique is necessary. The most common and effective approach that can be used to address this issue is machine learning, according to research. The most widely used machine learning methods include neural networks, decision trees, logistic regression, and support vector machines (SVM). A group of deep learning and machine learning models will be trained in this study to identify phishing websites.

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