Closed-Loop Threat Mitigation for Critical Infrastructure Digital Twins
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Solution Overview
Problem
Existing solutions for critical infrastructure security and resilience are limited by their inability to simulate new threats in production environments, leading to discrepancies and inefficiencies in threat response, and they lack the ability to automate threat management and simulation across complex interconnected systems.
Innovation Solution
A closed-loop automated procedure using digital twins and root cause analysis to monitor, simulate, and mitigate cyber-physical threats, incorporating machine learning models and a security orchestrator to manage and adapt to real-time data, with a system architecture that includes a monitor, analyze, plan, and execute framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing solutions analyze historical data to identify unusual patterns, then threat detection capability is improved, but the ability to respond to new threats in production environment deteriorates
Solution Approach 1:
The system performs preliminary actions by simulating new threats in a virtual environment (digital twin) before they occur in the production system. This allows the machine learning models to be trained on synthetic attack patterns, enabling the system to detect and respond to new threats without exposing the actual production system to risk.
Solution Approach 2:
The invention creates a digital twin (copy) of the production system that replicates its behavior and characteristics. This copy is used to generate synthetic threat data and train detection models, allowing the system to learn about new threats through the copy without affecting the original production system.
2Measurement precision
If manual security testing and simulation are performed in development environment, then threat analysis capability is improved, but system productivity and real-time response deteriorate
Solution Approach 1:
The system performs self-service by automatically generating synthetic threat data, training machine learning models, and updating detection algorithms without requiring continuous manual intervention. The closed-loop system autonomously improves its threat detection capabilities while maintaining real-time monitoring and response in the production environment.
Solution Approach 2:
The invention replaces manual mechanical processes (security professionals manually testing and analyzing threats) with an automated system that uses machine learning models and digital twins. This substitution enables continuous real-time threat analysis without the productivity loss associated with manual security testing.
3Adaptability or versatility
If digital twins and simulation are used to train on new threats, then adaptability to new threats is improved, but device complexity increases
Solution Approach 1:
The digital twin serves multiple functions: it replicates the production system for training purposes, generates synthetic threat data, validates detection algorithms, and provides a safe environment for testing. This multi-functionality justifies the added complexity by eliminating the need for separate systems for each purpose.
4Reliability
If closed-loop automated procedure is implemented, then threat response effectiveness is improved, but loss of time for system setup and configuration increases
Solution Approach 1:
The closed-loop system continuously monitors production environment performance and automatically feeds this information back to update the digital twin and retrain machine learning models. This automated feedback mechanism eliminates the need for manual system reconfiguration and setup, allowing the system to adapt to new threats automatically while maintaining high response effectiveness.
Data Source
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AI summary
A method (300) for improving the resilience of a Critical Infrastructure (CI) (101), the method (300) being implemented in a system (100) for monitoring the Cl (101), the method (300) comprising: - collecting (301) real-time data comprising at least data from an Internet of Things (IoT) network (103) of the Cl (101); - identifying anomaly and recognizing threat (3042) using trained machine learning (ML) models, from the real-time data collected and by a detection and root cause analysis component (DRCA) (110) of the system (100); - informing (305) a reaction component (112) of the system (100), by the DRCA (110); - determining (306), by the reaction component (112), actions to mitigate the recognized threat; - informing (307) a security orchestrator component (114) of the system (100) of the threat; and - executing (3081) actions to mitigate the recognized threat, by the security orchestrator component (114), in the Cl (101).