NLP Security Data Search via Intermediary Translation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for detecting and mitigating malware are challenged by increasingly sophisticated obfuscation methods, particularly those using Domain Name System (DNS) queries for data exfiltration, necessitating improved detection and prevention methods.
Innovation Solution
The implementation of a natural language processing (NLP)-based system for global search of a security-related data store, which translates user queries into refined categories and constructs SQL statements to directly fetch data from a database, enhancing detection and prevention capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional search methods are used for security data, then system complexity is reduced, but search response time and accuracy deteriorate
Solution Approach 1:
The patent introduces an NLP service as an intermediary component between the user interface and the security data store. This service translates natural language queries into structured SQL statements, enabling fast and accurate search without requiring users to understand complex query languages or system internals. The NLP service acts as a mediator that handles the complexity of data translation while maintaining simple user interaction.
Solution Approach 2:
The system is divided into distinct functional modules: an NLP service for query translation, a database layer for data storage, and an application layer for processing results. This segmentation allows each component to specialize in its function, improving overall search performance while distributing complexity across manageable modules rather than concentrating it in a single monolithic system.
2Measurement precision
If natural language processing is implemented, then search accuracy and user experience improve, but system complexity and maintenance costs increase
Solution Approach 1:
The NLP service is designed as a universal component that handles multiple types of security-related queries through a single interface. It can process various natural language formulations and translate them into appropriate database queries, maintaining high search accuracy across different query types without requiring separate processing logic for each case. This multi-functionality reduces the need for multiple specialized systems.
Solution Approach 2:
The system includes automatic query optimization and result retrieval mechanisms that operate without manual intervention. The NLP service automatically translates queries, the database engine optimizes query execution, and results are automatically formatted and returned. This self-service capability reduces maintenance burden despite the added complexity of NLP processing.
3Adaptability or versatility
If data is stored in multiple formats for versatility, then adaptability improves, but data consistency and maintenance difficulty worsen
Solution Approach 1:
The NLP service acts as an intermediary that handles data format translation automatically. It receives natural language queries and translates them into structured SQL statements that work with the underlying database schema. This mediator layer allows the system to support multiple data representations while maintaining a consistent internal storage format, reducing maintenance complexity.
Solution Approach 2:
The system uses parameterized queries and dynamic schema mapping to adapt to different data formats without changing the core database structure. By changing query parameters and translation rules rather than fundamental data storage, the system maintains versatility while preserving data consistency and simplifying maintenance.
Data Source
AI summary
Techniques for global search of a security related data store using natural language processing are disclosed. In some embodiments, a system, a process, and/or a computer program product for global search of a security related data store using natural language processing includes receiving a query for a security related data store; translating the query using a natural language processing (NLP) classifier to extract one or more categories; performing another query of one or more tables in the security related data store based on the one or more categories; and returning results for the query based on the another query of the one or more tables in the security related data store based on the one or more categories.