Machine Learning Supported Malicious URL Detection

Due to the lack of security knowledge, many web applications experience numerous web assaults. The security of online web applications must therefore be increased by precisely identifying malicious URLs. Hackers today generally target end-to-end technology and take advantage of human weaknesses. These methods include pharming, phishing, and social engineering, among others. These attacks involve a number of methods, one of them is to trap clients using malicious Uniform Resource Locators (URLs). Because of this, Nowadays detecting malicious URLs is a hot topic. There are several scientific papers demonstrating various techniques in order to identify dangerous URLs using machine learning. Based on our proposed URL behaviours and features, we provide a machine learning-based solution for detecting malicious URLs in this study. In general, a new set of URL properties and behaviors, along with a machine learning algorithm, compose the suggested detection method. The experimental outcomes indicate that the suggested URL features and behaviour can considerably increase the proficiency to recognise malicious URLs. This points to the fact that the proposed technique can be viewed as an effective and user-friendly method of identifying dangerous URLs.