Event Profile Comparison for Data Source Validation
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Solution Overview
Problem
Computing devices connected to the Internet are susceptible to cyberattacks due to security vulnerabilities, and existing anomaly detection methods may fail to accurately identify security issues if data sources do not properly populate event fields with data, leading to misconfiguration or version mismatches.
Innovation Solution
A method to determine whether data sources properly process raw events by generating event profiles that indicate the percentage of populated event fields, allowing for accurate anomaly analysis by comparing these profiles to reference profiles, and reconfiguring the system if necessary to ensure data fields are correctly populated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data sources process raw events to generate processed events for anomaly analysis, then anomaly detection capability is improved, but data source misconfiguration or version mismatch causes event fields to be improperly populated, leading to detection failures
Solution Approach 1:
The system performs preliminary validation by comparing event profiles against reference profiles before anomaly analysis. This proactive approach identifies misconfigurations and version mismatches in data sources before they cause detection failures, ensuring event fields are properly populated upstream in the processing pipeline.
Solution Approach 2:
The system implements feedback by continuously monitoring event profile characteristics and comparing them to expected reference profiles. When deviations are detected indicating misconfiguration, the system generates feedback signals to alert operators and can automatically trigger reconfiguration processes, creating a closed-loop system that maintains data quality.
2Reliability
If the system implements comprehensive event profile comparison and validation, then data source misconfiguration is detected, but system complexity increases due to additional processing steps
Solution Approach 1:
The system applies partial validation by focusing on critical event profile characteristics and reference comparisons rather than exhaustive analysis of all possible parameters. This selective approach detects misconfigurations effectively while avoiding the complexity overhead of comprehensive validation of every event field and processing parameter.
3Measurement precision
If event profiles are continuously monitored and compared to reference profiles, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs partial comparisons by focusing on key event profile characteristics and essential reference parameters rather than exhaustive analysis of all event fields. This selective validation approach maintains high measurement precision for critical detection accuracy while minimizing the time and computational resources required for profile comparison.
Data Source
AI summary
An event profile corresponding to a data source at a target system is determined. The event profile includes, for each of a number of fields, a percentage of events that after processing by the data source include data in that event field. A reference event profile is determined that includes, for each of the event fields, a reference percentage. The event profile is compared to the reference event profile. Whether the data source properly processed the events is determined based on comparison of the event profile to the reference event profile.