API Vulnerability Detection Using Semantic Operation Sequencing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting cyber vulnerabilities in APIs are time-consuming, costly, and prone to inaccuracies due to manual operations, especially for complex APIs, requiring specialized cybersecurity experts.
Innovation Solution
A computer-implemented method and system that automates the detection of cyber vulnerabilities in APIs by parsing API specification files, semantically classifying operations, logically sorting sequences, and performing security tests using machine learning and attack modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual security testing methods are used for API vulnerability detection, then detection accuracy can be maintained through expert analysis, but detection time and costs increase significantly
Solution Approach 1:
The system segments the vulnerability detection process into distinct modules: API specification parsing, semantic classification of operations, logical sorting of operation sequences, and automated security testing. Each module handles specific aspects of the detection process, enabling parallel processing and reducing overall detection time while maintaining accuracy through specialized analysis at each stage.
Solution Approach 2:
The system replaces manual mechanical security testing with automated computational methods. Machine learning models perform semantic classification and logical sorting, while automated attack modules execute security tests without human intervention, dramatically reducing detection time while maintaining or improving accuracy through consistent, repeatable processes.
2Reliability
If manual penetration testing is performed by security experts, then comprehensive vulnerability analysis is achieved, but scalability is limited and costs increase
Solution Approach 1:
The system enables self-service vulnerability detection by automatically performing semantic classification, logical sorting, and security testing without requiring security experts for each API analysis. The automated processes can handle multiple APIs simultaneously, providing scalable vulnerability detection that maintains comprehensive analysis through systematic coverage of all API operations.
Solution Approach 2:
The system creates a universal vulnerability detection platform that can analyze various API types and operations through standardized processes. The semantic classification and logical sorting mechanisms are applicable across different API specifications, enabling the same system to reliably detect vulnerabilities in diverse APIs without requiring specialized manual analysis for each case.
3Productivity
If automated vulnerability scanning is implemented, then detection speed and scalability improve, but handling of complex API logic becomes challenging
Solution Approach 1:
The system performs preliminary semantic classification of API operations and logical sorting of operation sequences before executing security tests. By pre-processing the API specification to understand operation semantics and dependencies, the system prepares structured data that guides the automated testing process, enabling it to handle complex API logic systematically rather than through random or simplistic scanning.
Solution Approach 2:
The system introduces intermediate processing layers between the raw API specification and the security testing phase. Semantic classification and logical sorting act as intermediaries that transform complex API definitions into structured, understandable formats, allowing automated testers to navigate complex logic through predefined classification schemes and logical relationships rather than attempting to interpret raw specifications directly.
4Reliability
If comprehensive security testing is performed on all API operations, then vulnerability detection coverage is maximized, but testing time and computational resources increase
Solution Approach 1:
The system performs preliminary logical sorting of API operations to identify execution sequences and dependencies before conducting security tests. This pre-processing step allows the system to prioritize testing of critical operation sequences and understand contextual relationships, enabling comprehensive coverage of vulnerable paths while avoiding redundant testing of unrelated operations, thus reducing overall testing duration.
Data Source
AI summary
The present invention relates to a computer-implemented method for the automatic detection of cyber vulnerabilities in an application programming interface or API, for example having a Representational State Transfer or REST architecture, a GraphQL or gRPC architecture. Furthermore, the present invention also relates to a system for the automatic detection of cyber vulnerabilities in an application programming interface or API, in particular having a Representational State Transfer or REST architecture, gRPC or a GraphQL architecture as well as a computer program product for the automatic detection of cyber vulnerabilities in an application programming interface or API, in particular having a Representational State Transfer or REST architecture, a gRPC or a GraphQL architecture.