Dynamic Cyber Threat Modeling From Application Architecture
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Solution Overview
Problem
Web-based environments face challenges in identifying and preempting cyber threats and vulnerabilities due to the dynamic and complex nature of software applications, leading to potential data breaches and systemic vulnerabilities.
Innovation Solution
A system and method utilizing generative machine-learning models to generate dynamic cyber threat models based on software application architecture, predicting and generating threat scenarios, and dynamically reconfiguring applications to prevent vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static security models are used, then implementation simplicity is maintained, but security effectiveness deteriorates due to inability to adapt to dynamic threats
Solution Approach 1:
The patent implements dynamic threat models that automatically adapt to changing application architectures and threat landscapes. The system continuously updates threat models based on runtime observations, configuration changes, and new vulnerability information, transforming static security models into dynamic ones that evolve with the application environment.
Solution Approach 2:
The system employs machine learning models that automatically generate and update threat models without requiring manual security expert intervention. The models self-adjust based on observed patterns, application behavior, and threat intelligence, enabling the security system to service itself and adapt autonomously to new threats.
2Productivity
If manual threat identification methods are used, then false positive rates are reduced, but detection speed deteriorates due to time-consuming analysis
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated machine learning models. These models process application architecture data, code patterns, and threat intelligence at machine speed, dramatically increasing detection speed while maintaining high accuracy through sophisticated pattern recognition and false positive filtering algorithms.
Solution Approach 2:
The system implements continuous feedback loops where detection results, false positives, and new threat data are fed back into the machine learning models for retraining and refinement. This feedback mechanism allows the system to learn from mistakes and improve both detection speed and accuracy over time through iterative optimization.
3Reliability
If comprehensive security scanning is performed, then threat detection coverage is improved, but system performance deteriorates due to computational overhead
Solution Approach 1:
The system performs preliminary analysis during application development and deployment phases, creating baseline threat models before runtime. This preliminary action captures security requirements and architecture details when the application is static, reducing the need for intensive scanning during runtime and minimizing performance impact while maintaining comprehensive coverage.
Solution Approach 2:
The patent divides security scanning into segmented phases: static analysis during development, incremental updates during deployment, and targeted runtime monitoring. This segmentation allows comprehensive threat detection coverage to be achieved without requiring continuous full-system scanning, thereby preserving application performance while maintaining security effectiveness.
Data Source
AI summary
A system includes a memory configured to store a set of application environment parameters associated with a software application of a plurality of software applications. The system further includes processors for accessing the set of application environment parameters associated with the software application, identifying, based on the set of application environment parameters, a plurality of potential threats and vulnerabilities associated with an execution of the software application in accordance with the current configuration, and executing one or more generative machine-learning models trained to generate a prediction of one or more cyber threat scenarios based on the set of application environment parameters and the plurality of potential threats and vulnerabilities. The prediction of the one or more cyber threat scenarios includes cyber threat scenarios specific to the software application. The processors further output, by the one or more generative machine-learning models, the prediction of the one or more cyber threat scenarios.