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Malicious URL Detection using Logistic Regression

One of the major challenges faced by the Internet in the present day is to deal with achieving web security from ever-rising diverse types of threats. Machine learning algorithms offer promising techniques to detect malicious websites performing unethical anonymous activities on the Internet. Attackers have been found to continuously evolve with updated techniques to attack web users using malicious Uniform Resource Locators (URLs). The main objective of such attacks is to gain financial benefits through acquiring personal information. In the present research, a machine learning (ML)-based approach is proposed to identify malicious users from URL data. An ML model is implemented using Logistic Regression to detect malicious URLs. The data set used in the study is collected from well-known sources like PhishTank, Kaggle.com, and Github.com. Our novel framework is further evaluated against traditional malicious URL models and our results highlight positive steps forward of the proposed approach.