Detection and Analysis of Malicious Domains Using Machine Learning Techniques

Abstract

Cyber dangers, particularly bad domains used for phishing, virus distribution, spam, and unauthorized data access, have grown significantly due to the quick expansion of internet usage. Because new harmful websites are constantly appearing, traditional blacklist-based detection techniques are sometimes insufficient. Using domain-related characteristics and classification methods, this study suggests a machine learning-based hazardous domain detection system to efficiently identify dangerous domains. In order to train prediction models that can differentiate between harmful and lawful domains, the system makes use of a dataset that contains information about malicious domains. To increase prediction accuracy and decrease false positives, data preprocessing, feature extraction, and model training are used.By lowering dependency on manually maintained blacklists and facilitating early detection of dangerous domains, the suggested method improves cybersecurity. Results from experiments show that machine learning methods may effectively identify harmful domains with enhanced scalability and detection performance.