Temporal Correlation Logic for Out-of-Order Observable Processing
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Solution Overview
Problem
Conventional methods for processing information to detect and prevent threats are labor-intensive and ineffective, especially when data is received in non-chronological order, hindering timely and effective response to security threats.
Innovation Solution
A time-based system that uses temporal correlation logic to compare observables with process templates, determining compatibility through recursive processing of time-stamped information, allowing for automated threat detection and response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processing methods are used to analyze information from multiple sources, then flexibility in handling various formats is maintained, but processing efficiency and response time deteriorate
Solution Approach 1:
The patent replaces manual mechanical analysis with automated electronic processing systems. The system uses computers to automatically receive, store, and analyze information from multiple sources in various formats, eliminating the need for manual handling while maintaining the ability to process diverse data types through standardized digital interfaces.
Solution Approach 2:
The system performs self-service by automatically correlating and analyzing information without human intervention. The computer system autonomously receives data from multiple sources, stores it in databases, correlates information based on temporal relationships, and generates analyses, freeing operators from manual processing tasks.
2Speed
If data is processed without chronological ordering, then processing speed is improved, but accuracy in detecting security threats deteriorates
Solution Approach 1:
The system performs preliminary chronological ordering of data as it is received, assigning timestamps and organizing information in temporal sequences before analysis. This preliminary organization ensures that when the data is later analyzed for security threats, the chronological context is already established, maintaining accuracy without slowing down the overall processing speed.
Solution Approach 2:
The patent introduces temporal correlation databases and timestamp metadata as intermediary structures between raw data input and threat analysis. These intermediaries preserve chronological relationships without becoming bottlenecks, allowing the system to maintain both processing speed and detection accuracy by efficiently managing temporal data structures.
3Productivity
If automated processing systems are implemented, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the automated processing system into distinct functional modules: data reception components, database storage systems, temporal correlation engines, and analysis generators. Each module handles a specific aspect of processing, which reduces overall system complexity by making each component simpler and more specialized while maintaining high collective efficiency.
Solution Approach 2:
The system employs universal databases and standardized data structures that can handle multiple types of information from various sources through common interfaces. This multi-functionality reduces complexity by eliminating the need for separate specialized systems for different data types, allowing a single automated system to efficiently process diverse information.
4Reliability
If temporal correlation analysis is performed on all observables, then threat detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs temporal correlation analysis selectively rather than on all observables uniformly. It applies full temporal correlation only to data elements that show potential security relevance based on initial filtering, while applying lighter processing to routine data. This partial action approach maintains high detection accuracy for critical threats while reducing overall processing time.
Data Source
AI summary
According to one embodiment, an electronic device may be configured to perform temporal correlation operations to determine if a plurality of observables correspond to an event of interest. The electronic device comprises a memory and a processor. The memory is adapted to store information representing a process template for the event, where the process template including a plurality of observation states. The processor is coupled to the memory. The processor is adapted to receive a plurality of observables, even in non-chronological order, and to conduct a recursive comparison of time values associated with each of the plurality of observables to timing ranges associated with each observation state to determine if the plurality of observables are associated with the event.