Domain Name Analysis with Machine Learning : Enhancing Efficiency and Reducing Analyst Strain

This study aims to shed light on the current state of the domain analysis methodologies and proposes a practical implementation of a machine learning algorithm for the domain analysis to integrate within threat intelligence activities. The algorithm aims to reduce the workload on threat intelligence analysts by identifying false positive (i.e. domains that do not present risk for the customers) with high confidence, leaving only the suspicious domains for further manual investigation, achieving more efficient time and resource utilization by the cybersecurity analysts. The results obtained in the study aim to provide insights on the advantages and challenges of such implementation, as well as suggest further direction for improvement of automated methods in domain name analysis.