A Logical Development of Phishing Attack Detection Methodology by Using Cybersecurity Principles with Elevated Deep Learning Technology

Cybercriminals have taken advantage of phishing attacks to launch a variety of security breaches. In these attacks, bogus website Uniform Resource Locators (URLs) are sent out via email, messages, and other online mediums in an effort to trick users into giving over sensitive information. Various old methods for detecting phishing websites based on blacklists are used to forecast the phishing URLs. Nevertheless, since there are always new phishing websites popping up on the Internet, the old solutions that rely on blacklists cannot reliably forecast the majority of these sites. An improved method for detecting phishing attempts using deep learning has been suggested; it's dubbed Elevated Learning with Attack Detection Principle (ELADP). To test how well it works, ELADP is cross-validated with the traditional method known as Regression Network (RN). In this research, we provide a novel phishing detection system that uses a wide variety of deep learning approaches, including RN, attention networks, neural architecture, and recurrent neural networks, among others. Quick URL-based page classification is the system's main focus, which proves its effectiveness in real-time phishing assault detection. The usefulness of the suggested strategy is demonstrated by its evaluation using a huge dataset that contains approximately five million tagged URLs.