Malicious URL Detection Using Logistic Regression

Abstract

Abstract: Over the past few years, there has been a considerable expansion in both the amount and variety of web services. Online services like social networking, online gaming, and banking have quickly advanced along with people’s reliance on them for routine chores. As a result, a lot of information is added to the Web every day. These web services open up new ways for people to engage, but they also give thieves new chances. URLs serve as jumping off points for all types of web attacks, making it possible for any user with bad intent to submit a malicious URL and steal the identity of a legitimate individual. Malicious URL detection is a crucial task in ensuring the security of internet users. This study describes a novel logistic regression technique for identifying malicious URLs.. The proposed method leverages a dataset consisting of various features extracted from URLs and their associated labels indicating whether they are malicious or not. To extract features, we consider both structural and content based characteristics of URLs. Structural features include domain length, path length, and presence of special characters, while content based features involve examining the lexical composition of the URL, such as the presence of suspicious keywords or uncommon words. Using a labelled dataset, the logistic regression model is trained using the retrieved features. The likelihood that a given URL is malicious is then predicted using the trained model. Results from experiments show how effective is the suggested strategy. When identifying fraudulent or benign URLs, the logistic regression model performs with high accuracy.