Malicious URL Detector using Machine Learning

Abstract

In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user’s system and causing losses of billions of dollars each year. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. In this research paper we have implemented the application that it can check the URL given by the user is benign, defacement, malware, phishing. After checking then it will display the type of URL in user interface. The increasing sophistication of cyber threats, particularly in the form of malicious URLs, poses a significant challenge to internet security. Traditional signature-based approaches and heuristic methods have proven insufficient in detecting rapidly evolving malicious URLs.This research paper presents a comprehensive study on the development and evaluation of a malicious URL detector based on machine learning known and novel malicious URLs. Keyword: Phishing, URL, machine learning, cyber security, random forest, malicious.