Malicious-Attack Exposure Assessment via Field Data Analysis

The patent introduces attack report data as an intermediary element between the software component and the exposure level assessment. Instead of directly examining software configuration, the system uses third-party attack reports that document actual malicious attacks against similar software components. This intermediary data source provides objective, field-based evidence of real-world attack patterns, thereby improving measurement precision without requiring direct complex analysis of the software's internal state.

The patent creates a virtual model of exposure levels by copying and analyzing attack report data from multiple sources rather than directly measuring the actual software component's vulnerability. The system aggregates attack reports that contain information about malicious attacks, software identifiers, and exposure conditions, then uses this copied data to infer the exposure level of the target software component. This approach achieves high accuracy by leveraging existing attack data without requiring direct penetration testing or deep software analysis.

The patent performs preliminary actions by pre-aggregating and organizing attack report data into structured formats before actual exposure assessment is needed. The system collects attack reports containing software identifiers, attack types, and exposure information in advance, and organizes this data for efficient querying. When an exposure level assessment is required, the system can quickly retrieve and analyze pre-processed data rather than collecting and processing raw attack reports from scratch, significantly reducing processing time while maintaining high accuracy.

The patent creates a universal attack report database that serves multiple functions: it provides exposure level assessment data, identifies attack patterns, supports security trend analysis, and enables comparison across different software components. By building a multi-functional system that processes diverse attack report data in a unified framework, the patent achieves high assessment accuracy through comprehensive data analysis while avoiding redundant processing steps that would increase time consumption.

The patent extracts only the essential and relevant information from attack reports, such as software component identifiers, attack type classifications, exposure level indicators, and key attack characteristics. Instead of processing the complete raw attack report data which may contain extensive contextual information, the system selectively extracts and analyzes only the critical fields necessary for exposure level determination. This extraction approach maintains high reliability by focusing on the most informative data elements while significantly reducing the volume of data that requires processing.

The patent applies local quality by treating different types of attack report data with different levels of processing intensity. The system identifies and prioritizes key fields such as software identifiers and attack outcomes that directly impact exposure level assessment, applying rigorous analysis to these critical elements. For less critical contextual information, the system applies lighter processing or aggregation. This differentiated approach ensures high reliability for the most important assessment factors while reducing overall data processing requirements by not uniformly processing all data elements at the same level of detail.