Database Query Security Gateway Using GAN Metadata Validation
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Solution Overview
Problem
Rogue database queries, including SQL and NoSQL injection attacks, pose significant threats to system integrity, security, and performance by allowing unauthorized access to sensitive data, often originating from both external and internal sources.
Innovation Solution
A system utilizing a generative adversarial network (GAN) model protected by a security gateway to analyze query requests, generate metadata, and compare it with expected metadata to automatically block or allow queries, without executing the queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional query validation methods are used to prevent rogue queries, then security is improved, but system performance and computing resource usage deteriorate due to manual validation processes
Solution Approach 1:
The patent replaces manual query validation mechanisms with an automated machine learning model that analyzes query metadata and generates predictions about query legitimacy. The ML model substitutes human operators and manual validation processes, automatically comparing query metadata against learned patterns to determine whether queries are rogue or legitimate, thereby maintaining security while eliminating the performance overhead of manual validation.
Solution Approach 2:
The system implements self-service by enabling the database system to automatically validate queries through the ML model without requiring external manual intervention. The automated comparison of query metadata with model predictions allows the system to autonomously identify and block rogue queries, reducing dependency on human operators and improving processing efficiency while maintaining security standards.
2Reliability
If automated query blocking systems are implemented to prevent rogue queries, then security is improved, but device complexity increases due to additional validation components
Solution Approach 1:
The patent introduces an intermediary ML model that sits between query submission and database execution. This intermediary component analyzes query metadata and provides validation decisions without requiring complex integration with database internals. The ML model acts as a mediator that simplifies the overall system architecture by centralizing validation logic in a single, manageable component that can be trained and updated independently.
Solution Approach 2:
The system uses copying by creating and training the ML model on replicated query metadata patterns from historical data. Instead of implementing complex validation rules directly in the database system, the approach copies and analyzes metadata patterns from numerous queries to build a predictive model. This copying strategy simplifies the validation mechanism by relying on pattern recognition rather than complex rule-based systems.
3Measurement precision
If query metadata analysis is performed to identify rogue queries, then measurement precision is improved, but loss of time occurs due to additional processing steps
Solution Approach 1:
The patent applies preliminary action by pre-training the ML model on extensive query metadata patterns before deployment. The model learns from historical query data and establishes baseline patterns of legitimate versus rogue queries in advance. During actual query validation, the pre-trained model can quickly compare new query metadata against learned patterns, achieving high measurement precision without requiring time-consuming analysis during query processing.
Solution Approach 2:
The system implements partial action by focusing the ML model's analysis on specific critical metadata fields rather than examining every aspect of each query. The model identifies and analyzes only the most relevant metadata characteristics that differentiate rogue from legitimate queries, achieving sufficient measurement precision while reducing processing time by avoiding exhaustive analysis of all query attributes.
Data Source
AI summary
Systems, computer program products, and methods are described herein for improving network and data security by automatically preventing rogue database queries. The present disclosure is configured to identify a query request, wherein the query request comprises query request metadata; apply the query request to a generative adversarial network (GAN) model, wherein the GAN model is protected by a security gateway; generate, by the GAN model, a result metadata based on the query request, wherein the GAN model is trained on a database schema; and determine, based on a comparison of the query request metadata and the result metadata, whether to allow the query request by the security gateway.