Computing Interface Parameter Detection for Active Vulnerability Testing
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Solution Overview
Problem
Existing cybersecurity solutions struggle to identify and preemptively test for vulnerabilities in computing interfaces, particularly APIs, during the pre-production phase, due to a lack of sufficient data about interface logic and dependencies, leading to potential exposure to malicious entities.
Innovation Solution
Implementing a machine learning model to identify parameter-indicating portions of requests and resolve dependencies between computing interfaces, allowing for active testing even when the interfaces are offline, using techniques such as entropy analysis and dependency graph creation to determine proper request formatting and test for vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If runtime data collection is used to detect API behavior, then vulnerability detection capability is improved, but response time deteriorates due to the rapid pace of cyberattacks
Solution Approach 1:
The system performs vulnerability detection in advance during the development phase by analyzing code models and generating test cases before the API is deployed to production. This preliminary action allows vulnerabilities to be identified and corrected before they can be exploited by attackers, eliminating the time loss associated with runtime detection.
Solution Approach 2:
The system creates a virtual copy or model of the API code structure and analyzes this model to generate test cases. By working with a copy of the code rather than the live production API, the system can perform comprehensive vulnerability analysis without impacting actual API operations or introducing delays in response to attacks.
2Measurement precision
If comprehensive API testing is performed, then vulnerability detection accuracy is improved, but testing complexity increases
Solution Approach 1:
The testing process is segmented into multiple phases: code model generation, test case generation based on the model, and execution of tests. This segmentation allows comprehensive testing to be broken down into manageable steps, each focusing on specific aspects of the API, thereby reducing overall testing complexity while maintaining high detection accuracy.
Solution Approach 2:
The system automatically generates test cases by analyzing the API code model itself, without requiring manual test case creation. This self-service approach leverages the code model to produce comprehensive test scenarios automatically, reducing testing complexity while maintaining thoroughness in vulnerability detection.
3Object-affected harmful factors
If pre-production testing is implemented, then security risk is reduced, but development time increases
Solution Approach 1:
The system performs all necessary vulnerability testing and security validation during the development phase before production deployment. By completing security checks in advance, the system eliminates the need for separate pre-production testing phases, thereby reducing development time while still achieving comprehensive security validation.
Solution Approach 2:
The vulnerability detection and testing processes are integrated continuously into the development workflow rather than being separate discrete phases. This continuous integration ensures that security testing occurs naturally throughout development without adding additional time, as the same code is analyzed multiple times during different development activities.
Data Source
AI summary
Systems and methods for active parameter identification. An example method includes applying a machine learning model to features extracted from each of at least one request to a computing interface, wherein the machine learning model is trained per value using a training set including a plurality of training values of a plurality of training requests, wherein the machine learning model is trained to output an indicator as to whether each portion of a request containing a respective value indicates a parameter when applied to the request; and identifying at least one parameter-indicating portion of each request to the computing interface based on outputs of the machine learning model.