Semi-Dynamic Vulnerability Detection With Digital Twin Mitigation
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Solution Overview
Problem
Conventional vulnerability detection methodologies are inadequate for addressing the ever-changing threats faced by enterprise organizations, failing to detect emerging vulnerabilities due to their focus on day-to-day operational risks and insensitivity to market volatility and new hacking methods.
Innovation Solution
A computing platform utilizing a generative adversarial network (GAN) and spiking neural network (SNN) to analyze workflow data, generate a knowledge graph for mitigation action plans, and validate these plans through a digital twin using a pseudo-node back-tracking reconciler to ensure accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static vulnerability detection approaches are used, then conventional operational risks can be detected, but emerging vulnerabilities driven by changing global situations and new hacking methods cannot be detected
Solution Approach 1:
The patent applies dynamics by transitioning from static vulnerability detection to a dynamic approach using generative adversarial networks (GANs) that continuously learn from new data. The system dynamically adapts its detection models to emerging vulnerability patterns by training on real-time workflow data, enabling it to detect new hacking methods and changing global situation impacts without manual reconfiguration.
Solution Approach 2:
The system implements feedback mechanisms where detected vulnerabilities and their mitigations are fed back into the GAN training process. This continuous feedback loop allows the model to refine its understanding of vulnerability patterns and improve its detection accuracy over time, addressing the adaptability issue by learning from past detections to better identify emerging threats.
2Productivity
If conventional assessment methodologies focused on day-to-day operational risks are used, then routine vulnerabilities can be identified, but emerging vulnerability patterns driven by market volatility and new phishing methods remain undetected
Solution Approach 1:
The system performs self-service by automatically training and retraining its GAN models on incoming workflow data without requiring manual intervention. The automated training process continuously updates the vulnerability detection capabilities to match emerging patterns, maintaining high productivity while adapting to new threats through self-improving algorithms.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the training parameters and architecture of the GAN models based on the complexity and nature of detected vulnerabilities. This allows the system to optimize its assessment efficiency for routine vulnerabilities while automatically adapting parameters to handle complex emerging patterns like new phishing methods and market volatility impacts.
3Speed
If AI models are used to detect vulnerabilities in real-time, then detection speed improves, but computational resource consumption increases
Solution Approach 1:
The system applies partial action by selectively deploying AI computing resources only when and where needed. The GAN models process workflow data in batches and only intensify computational analysis when vulnerability patterns are detected, rather than continuously consuming high computational resources. This maintains real-time detection speed while optimizing resource usage by applying AI intensity only to critical detection moments.
Data Source
AI summary
Arrangements for providing semi-dynamic vulnerability detection are provided. In some aspects, a computing platform may receive work flow data from one or more systems and may analyze the work flow data using a GAN. The GAN may output a potential vulnerability identified in the data, and a category of the potential vulnerability. Based on the potential vulnerability and the category, the computing platform may determine a severity of the potential vulnerability. An ANN-SNN converter may be executed to output a knowledge graph including a plurality of nodes forming a mitigation action plan for the potential vulnerability. The computing platform may generate a digital twin of the knowledge graph and may then reconcile the digital twin by back tracking through each node to validate each node of the digital twin. Based on the digital twin being reconciled, the generated mitigation action plan may be transmitted to a computing system for execution.