Input Validation API Using ML and Database Framework
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for preventing SQL injection attacks require extensive application logic for validating input requests at multiple levels, which is inefficient and resource-intensive.
Innovation Solution
A system that uses an API to validate input requests at the client device, server, and database levels independently, employing a validation service to generate and transmit data objects for validation, thereby avoiding the need for extensive programming and reducing computational resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If application logic is used to validate inputs at client device, server, and database levels, then security against SQL injections is improved, but computational resource consumption and time consumption increase significantly
Solution Approach 1:
The patent segments the validation function into separate validation services for each layer (client device validation service, server validation service, database validation service). Each validation service independently validates inputs at its respective layer without requiring extensive application logic, thereby maintaining security while reducing computational overhead at each segment.
Solution Approach 2:
The patent introduces validation services as intermediary components between the layers. These validation services act as mediators that handle the validation logic centrally, allowing the client device, server, and database to validate inputs without each requiring full application logic, thus reducing overall computational resource consumption.
2Reliability
If application logic is used to validate inputs at multiple levels, then security against SQL injections is improved, but the complexity of programming increases
Solution Approach 1:
The patent divides the validation functionality into separate validation services at each layer (client device validation service, server validation service, database validation service). This segmentation allows each layer to have its own dedicated validation logic rather than requiring complex application logic to be distributed across all layers, simplifying the programming at each individual layer.
Solution Approach 2:
The validation services serve as intermediary components that centralize the validation logic. Instead of embedding complex validation application logic throughout the system, the intermediaries handle validation centrally at each layer, reducing programming complexity while maintaining multi-level security.
3Reliability
If conventional validation methods are used, then security validation is achieved, but time consumption increases due to extensive programming requirements
Solution Approach 1:
The patent segments validation into independent validation services at each layer that can operate concurrently or independently. This allows validation to proceed without the sequential overhead of extensive application logic processing at each layer, reducing time consumption while maintaining effective validation.
Solution Approach 2:
The validation services act as intermediaries that streamline the validation process. By introducing these intermediary validation components, the system achieves effective security validation without the time-consuming requirement of extensive application logic processing at each layer.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for validating input requests. In an embodiment, an application program interface (API) can receive a input request from a client device or server. The API can validate the input requests based on client-side or server-side technology implemented by the client device or server. In response to validating a input request from the client device and server, the database's framework can validate the database request before processing the input request. In response to failing to validate an input request at the client device, server, or database level, a machine-learning algorithm can be used to determine how to resolve a possible error, which may have caused the failure to validate the input request.