Hybrid Machine Learning and Deep Learning Model for Malicious URL Detection

The World Wide Web has grown to be a vital component of our daily lives for the exchange of information and the sharing of knowledge. It facilitates timely, quick, and simple information transactions. Theft of identification and fraud involving identity are two forms of cybercrime in which hackers and malicious users take the personal data of authorized users in an attempt to perpetrate fraud or deception for financial gain. Because rogue URLs host unwanted material (email spam, scams, brute force flaws, etc.) to trick gullible people into falling for schemes (money loss, private information theft, malicious software deployment, etc.). Systems should be quick, accurate, and able to identify new malicious content in order to detect such crimes. Traditionally, blacklists are used primarily for this detection. Blacklists, however, are not all-inclusive and are unable to identify recently created dangerous URLs. In an effort to increase the breadth of malicious URL detectors, machine learning techniques have been the focus of more study in recent years. In order to determine whether a URL is good or poor, employ a straightforward technique in this work, which contrasts with SVM and LR, two additional methods.