Security Layer for Non-Relational Database Injection Mitigation
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Solution Overview
Problem
Non-relational databases, such as NOSQL, are vulnerable to injection attacks due to new query techniques like JSON-based queries and parameterized function calls, which simplify database access but introduce vulnerabilities that can compromise data confidentiality, integrity, and availability.
Innovation Solution
Integrating a security layer with the database driver layer to implement a learning-based mitigation strategy, where a trigger sets a learning phase to identify and update security patterns, and an execution phase to validate queries against these patterns, automatically blocking non-matching queries to prevent injection attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simplified query techniques (JSON-based queries, parameterized function calls) are introduced to non-relational databases, then database access becomes more straightforward and easier to use, but vulnerabilities to injection attacks increase
Solution Approach 1:
The patent introduces a security layer as an intermediary component between the application and the non-relational database. This security layer intercepts queries before they reach the database, validates them against learned security patterns, and blocks malicious injection attempts while allowing legitimate simplified queries to pass through. The security layer acts as a mediator that preserves the ease of use of simplified query techniques while filtering out harmful injection attacks.
Solution Approach 2:
The patent implements a learning phase during which the security layer预先 learns valid query patterns from legitimate application queries. This preliminary action builds a baseline of acceptable query structures before production use. When injection attacks occur, the security layer compares incoming queries against these pre-learned patterns to identify and block anomalies, enabling proactive protection rather than reactive response.
2Reliability
If security validation is implemented to protect against injection attacks, then data confidentiality, integrity, and availability are improved, but query processing complexity increases
Solution Approach 1:
The security layer is designed to automatically learn valid query patterns from the application's actual usage during a learning phase, without requiring manual configuration or developer intervention. The system self-adapts to the specific query patterns used by the application, making the security validation process transparent and reducing the perceived complexity for developers while maintaining strong security protection.
Solution Approach 2:
The patent transitions the security validation approach from static, pre-defined rules to dynamic, learned patterns that adapt to the application's specific query usage. By changing the parameter from fixed security rules to flexible, learned query patterns, the system achieves better security coverage with lower complexity, as the validation logic automatically adjusts to the application's actual behavior rather than requiring manual rule configuration.
3Reliability
If manual security pattern creation is performed by developers, then security protection can be implemented, but human error and omissions increase and developer effort is required
Solution Approach 1:
The security layer automatically learns valid query patterns by monitoring and analyzing legitimate application queries during a learning phase. This self-service approach eliminates the need for developers to manually create security patterns, reducing human error and omissions while maintaining comprehensive security coverage. The system autonomously adapts to the application's specific query patterns without developer intervention.
Solution Approach 2:
The system implements a feedback mechanism where the security layer continuously monitors legitimate application queries during the learning phase and uses this feedback to build accurate security patterns. This automated feedback loop ensures that the security protection is tailored to the specific application's usage patterns, improving reliability while eliminating manual pattern creation efforts.
Data Source
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AI summary
For mitigation of injection security attacks against non-relational databases, a database driver layer is integrated with a security layer. A trigger associated with the security layer is set to implement a learning phase of the security layer. In response to enabling the trigger, queries and query parameters associated with the respective queries are received. For the queries, a previously-stored security pattern is identified based on the query and the associated query parameters. The trigger associated with the security layer is reset to implement an execution of the security patterns. In response to resetting the trigger, an additional query and additional query parameters associated with the additional query is received. A particular security pattern is identified that is associated with the additional query and the additional query parameters. At least one of the additional query parameters is determined to not match a corresponding query parameter of the particular security pattern.