Automated Security Test Scenario Update via Neural Network
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Solution Overview
Problem
Current computer security testing systems require extensive and time-consuming human interaction to update security test scenarios when the configuration of elements in an information system changes, making them inefficient and labor-intensive.
Innovation Solution
A method and system that automatically generate security test scenario parameters using historical data and a trained neural network, converting configuration embeddings into test scenario embeddings to dynamically update test scenarios based on new system configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human interaction is used to update security test scenarios when system configuration changes, then the test scenarios can be manually adjusted and reviewed, but the process becomes intensive and time-consuming
Solution Approach 1:
The system automatically updates security test scenarios by monitoring configuration changes in the information system under test and triggering automated scenario generation based on detected changes, eliminating the need for manual human intervention in the update process
Solution Approach 2:
The system continuously monitors the actual configuration of the information system under test and compares it with expected configurations, automatically triggering updates to security test scenarios when mismatches are detected, creating a closed-loop feedback mechanism
2Reliability
If manual updating of security test scenarios is performed, then human expertise can be applied to complex scenarios, but the process is labor-intensive and inefficient
Solution Approach 1:
The system automatically generates and updates security test scenarios by monitoring configuration changes in the information system under test and triggering automated scenario generation based on detected changes, eliminating the need for manual human intervention in the update process
Solution Approach 2:
The patent replaces manual mechanical processes with automated neural network-based systems that process configuration data and generate test scenarios automatically, substituting human cognitive work with AI-driven automation
3Measurement precision
If security test scenarios are manually updated, then detailed review and validation can be performed, but the process requires intensive human interaction
Solution Approach 1:
The system automatically updates security test scenarios by monitoring configuration changes in the information system under test and triggering automated scenario generation based on detected changes, eliminating the need for manual human intervention in the update process
Solution Approach 2:
The patent introduces an intermediary neural network system that processes configuration data and generates test scenarios automatically, acting as a mediator between configuration changes and test scenario updates without requiring direct human intervention
Data Source
AI summary
The present disclosure relates to a system and method of automatically updating change test scenarios based on historical data about the system under test (SUT), its elements, their properties, testing environment, its characteristics and testing steps with their settings using AI. Once the AI has enough historical data, every time a change is made to the SUT, its elements, their properties, or at least one characteristic of the testing environment, the AI system makes a recommendation to update at least one setting of at least one test step in testing scenario.