Cyber Threat Query Generation for Variant Malware Detection

The system performs preliminary actions by collecting and analyzing malware samples, attack techniques, and attacker information in advance to build comprehensive databases. This preliminary data collection and analysis enables the system to detect new and variant malware through pattern recognition and correlation analysis, resolving the contradiction between detection accuracy for known malware and adaptability to new threats.

The system creates standardized copies and representations of malware characteristics, attack techniques, and attacker profiles through structured data models. By copying and standardizing threat information into reusable formats with unique identifiers, the system maintains detection accuracy while improving adaptability to new threats through efficient pattern matching and correlation.

The system segments threat analysis into multiple specialized components: malware sample analysis, attack technique analysis, attacker information analysis, and correlation analysis. This segmentation allows AI to efficiently process mass-produced malware while dedicated correlation analysis components handle sophisticated APT and targeted attacks by linking multiple data points, thereby maintaining both productivity and reliability.

The system creates a universal threat information processing framework that handles multiple attack types through integrated analysis. By universalizing the data collection and correlation mechanisms to work across different threat scenarios (mass-produced malware, APT, targeted attacks), the system maintains high productivity while ensuring reliability through comprehensive multi-functional analysis capabilities.