Innovative Approach to Cyber Threat Detection

This research article presents a novel approach to cyber threat detection that addresses the challenges of traditional data storage and management methods. It outlines the inefficiencies associated with conventional Security Operations Centers (SOCs) that rely on aggregating vast amounts of raw data, leading to high storage costs and hindered threat detection capabilities. The proposed methodology combines graph and relational databases to store only distinct data elements while preserving essential contextual relationships. By utilizing a graph database for relationships and a relational database for identifiers, the approach reduces data redundancy and enhances the ability to detect cyber threats. The findings indicate that relevant data is primarily identified within the initial weeks, with subsequent data often being duplicative. This innovative framework aims to optimize storage resources and improve analytical capabilities, thereby offering a practical solution for cybersecurity operations. The research contributes to the information security literature by bridging gaps in current data storage and analysis techniques.