NLP Threat Log Integration for Computing Security
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Solution Overview
Problem
Conventional threat detection systems are functionally disconnected from threat log repositories, necessitating a more efficient integration of threat data across multiple channels.
Innovation Solution
A system utilizing integrated natural language processing (NLP) and event analysis, which includes a memory device with executable code to access threat log data, perform preprocessing, tokenization, syntactic, and semantic analysis, identify events and entities, and update channel-specific threat detection models using machine learning, thereby extracting and integrating threat patterns from various sources like voice and written recordings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional threat detection systems are used, then the system structure is simple, but the system is functionally disconnected from threat log repositories and cannot efficiently integrate threat data
Solution Approach 1:
The patent merges the threat detection system with threat log repositories through an event analysis computing system that directly accesses and processes threat log data. This integration combines previously disconnected functional elements into a unified system, enabling efficient threat data incorporation while maintaining manageable complexity through systematic architecture design.
Solution Approach 2:
The event analysis computing system performs multiple functions: accessing threat log data, performing NLP preprocessing, conducting syntactic and semantic analysis, identifying events and entities, and updating detection models. This multi-functional approach enables the system to handle various threat data types and perform comprehensive analysis within a single integrated platform.
2Measurement precision
If comprehensive NLP analysis is performed on threat log data, then threat detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The NLP analysis process is segmented into distinct sequential stages: preprocessing, tokenization, syntactic analysis, and semantic analysis. Each stage processes specific aspects of the threat log data independently, allowing for optimized computation at each step and enabling parallel processing where applicable, thus reducing overall processing time while maintaining comprehensive analysis.
Solution Approach 2:
The system performs preliminary preprocessing and tokenization of threat log data before conducting detailed syntactic and semantic analysis. This preliminary action prepares the data in advance, organizing it into manageable units and removing irrelevant information, which accelerates subsequent analysis stages and reduces computational burden during critical detection phases.
3Reliability
If multiple analysis paradigms are applied to threat logs, then pattern recognition capability is enhanced, but system complexity increases
Solution Approach 1:
The system dynamically adapts its analysis approach based on the specific characteristics of threat log data and identified patterns. The event analysis computing system adjusts the depth and type of NLP processing applied to different data sources, optimizing the balance between comprehensive pattern recognition and manageable processing complexity for each specific threat scenario.
Data Source
AI summary
A system for integrated natural language programming (“NLP”) and event analysis provides threat detection in computing systems. In particular, the system may use an NLP unit to analyze threat logs from various sources according to multiple different metrics and/or analysis paradigms. Upon completing the analysis, the system may extract, via machine learning, event and/or threat patterns which may be integrated into the system's threat detection processes.