Log Analysis System Segmentation for Security Monitoring
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Solution Overview
Problem
Existing log analysis systems face inefficiencies in analyzing large volumes of data, where only a small number of processes require specific quality extraction, necessitating the need for targeted analysis to enhance monitoring and communication security.
Innovation Solution
A log analysis system comprising multiple processor units for normalization, allocation, common information addition, correlation analysis, and determination processing, which narrows down detection logs into specific quality targets and outputs them in a predefined format for efficient analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all detection logs are analyzed in detail, then analysis completeness is improved, but analysis time and computational resources are excessively consumed
Solution Approach 1:
The log analysis system segments the analysis process into multiple stages: initial filtering to identify candidate logs, detailed analysis of only those candidates, and hierarchical processing where different log types receive different levels of analysis depth. This segmentation allows the system to maintain analysis completeness for critical logs while reducing overall analysis time by skipping detailed analysis of obviously normal logs.
Solution Approach 2:
The system applies different analysis qualities to different log segments based on their characteristics. High-priority logs (e.g., authentication failures, error logs) receive comprehensive detailed analysis, while routine operational logs receive lighter processing. This local quality approach ensures that resources are concentrated on logs most likely to contain security issues, improving both completeness and efficiency.
2Measurement precision
If detailed analysis is performed on all logs, then detection accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The system performs preliminary filtering and classification of logs before detailed analysis. Quick assessment rules evaluate basic log characteristics to identify candidates for detailed analysis. This preliminary action eliminates obviously normal logs early in the process, allowing detailed analysis to be applied only to suspicious logs, thereby maintaining detection accuracy while improving processing speed.
Solution Approach 2:
The system applies partial analysis to the majority of logs (focusing on key fields and patterns) and excessive/detailed analysis only to a small subset of suspicious logs. This differentiated approach ensures that detection accuracy is maintained for critical cases while processing speed is improved for the overall log volume by avoiding exhaustive analysis of all logs.
3Adaptability or versatility
If comprehensive log collection is implemented, then monitoring coverage is improved, but data volume and storage requirements increase
Solution Approach 1:
The system extracts and collects only the essential and relevant log fields required for security analysis, rather than storing complete log records. By identifying and extracting key parameters (timestamps, source/destination addresses, protocol types, error codes), the system maintains comprehensive monitoring coverage across multiple log sources while significantly reducing the volume of data that needs to be stored and processed.
Solution Approach 2:
The log data is segmented into hierarchical levels: collected logs (comprehensive coverage), analyzed logs (filtered subset), and alert logs (critical events only). This segmentation allows the system to maintain wide monitoring coverage at the collection stage while reducing data volume at subsequent stages by eliminating redundant and obviously normal entries, thus balancing coverage and storage requirements.
Data Source
AI summary
A log analysis system includes a first processor unit, a second processor unit, a third processor unit, and a fourth processor unit. The first processor unit normalizes the detection log acquired by an acquirer, allocating the detection log into predefined monitoring target units, and outputs the monitoring target units. The second processor unit appends common information based on a predefined rule to each of the monitoring target units of the detection log output from the first processor unit, arranges the monitoring target units into information granularities based on the content of the detection log and common information, and outputs the monitoring target units as analysis unit information. The third processor unit gathers the analysis unit information output from the second processor unit and setting detection target event candidates based on a predefined rule, and outputs the detection target event candidates and the determination results.