Cyber Threat Information Processing Using Natural Language Models

The system performs preliminary actions by pre-defining attack techniques and building knowledge bases before threats occur. It analyzes malware samples in advance to extract features and patterns, creating a foundation for detecting both known and unknown threats. This preliminary preparation enables the system to recognize new malware variants based on their attack technique characteristics rather than requiring pre-existing specific patterns.

The system changes the detection parameters from static pattern matching to dynamic attack technique analysis. Instead of relying on fixed malware signatures, it transforms malware characteristics into standardized attack technique parameters, allowing flexible detection across different malware variants and types while maintaining consistent identification criteria.

The system performs preliminary action by pre-establishing attack technique knowledge bases and analysis frameworks before encountering new malware. It prepares standardized classification criteria and attack technique definitions in advance, enabling rapid identification of new variants without requiring retraining of AI models. This preliminary knowledge structure allows the system to handle novel threats effectively.

The system introduces attack technique analysis as an intermediary layer between raw malware data and final detection conclusions. This intermediary framework translates diverse malware characteristics into standardized attack technique categories, serving as a mediator that enables consistent analysis and response to new variants while maintaining the productivity benefits of AI processing.

The system implements universality by establishing a common attack technique knowledge base that serves multiple functions: local rapid detection, standardized description generation, and centralized coordination. This universal framework operates at both local and central levels, enabling decentralized quick response while maintaining consistent standardized descriptions through the shared knowledge structure.

The system employs feedback mechanisms where local detection results are fed back into the centralized knowledge base, and standardized attack technique descriptions are continuously refined based on actual detection data. This feedback loop ensures that local response speed is maintained while progressively improving standardization through real-world validation and correction of attack technique classifications.

The system applies segmentation by dividing malware analysis into distinct components: malware identification, attack technique characterization, and attacker attribution. This segmentation allows each component to be analyzed and identified independently with specialized methods, improving overall precision while enabling comprehensive identification of all three elements rather than focusing on malware alone.

The system adds another dimension to malware analysis by transitioning from single-dimensional malware signature matching to multi-dimensional attack technique analysis. This dimensional expansion incorporates attacker behavior patterns, attack methodology, and contextual information alongside traditional malware characteristics, enabling simultaneous identification of malware, attackers, and techniques through enriched analysis layers.