AUFIA: Adaptive URL Feature Intelligence for Malicious Website Detection using Machine Learning

Abstract

The high rate of internet service expansion has posed a great challenge due to a high number of malicious sites that conduct internet activities that include phishing, malware downloads and online fraud. These threats must be detected at the initial stage and improve security to make people resistant to possible threats. This paper introduces a machine learning-based system to identify malicious websites based on the URL-based features. The suggested method is aimed at deriving both lexical and structural attributes of URLs, such as URL length, the use of special characters, subdomains and protocols. They are the characteristics based on which classification models that can differentiate between benign and malicious URLs are trained. A smart detection system, which is known as the Adaptive URL Feature Intelligence Algorithm (AUFIA), is proposed to enhance analyzing features with the use of feature weighting and normalization algorithms. The experimental assessment proves that the given approach has high detection accuracy and better classification performance than some of the existing methods. The results suggest that feature engineering and machine learning methods are effective in offering scalable and effective solutions to the problem of malicious websites.