ML Asset Criticality Scoring in Industrial Control Networks
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Solution Overview
Problem
Industrial control systems (ICS) are vulnerable to cyber threats, which can cause physical damage, operational disruptions, and data breaches, highlighting the need for improved cybersecurity and asset criticality assessment.
Innovation Solution
A machine learning (ML) technique is employed to select, categorize, and assign scale factors to asset factors within an industrial control network, creating clusters and determining centroids to categorize asset criticality, using Euclidean distance and ML models for precise asset criticality scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cybersecurity methods are used to assess asset criticality in industrial control networks, then the assessment process is simple and manual, but the accuracy and real-time capability of the assessment are insufficient
Solution Approach 1:
The patent replaces manual, mechanical assessment methods with an automated machine learning system. The ML model automatically processes asset data, performs clustering analysis, and generates criticality scores without human intervention, thereby improving accuracy while managing complexity through automation.
Solution Approach 2:
The system enables self-service assessment by allowing assets to be automatically evaluated based on their own characteristics and network relationships. The ML model autonomously performs data collection, feature extraction, clustering, and scoring without requiring external manual analysis, making the assessment process self-sufficient and scalable.
2Measurement precision
If comprehensive asset factors are analyzed to improve criticality assessment accuracy, then the assessment becomes more precise, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the comprehensive asset assessment into distinct phases: data collection, feature extraction, clustering analysis, and criticality scoring. Each phase processes specific aspects of asset information independently, reducing the computational burden of handling all factors simultaneously while maintaining comprehensive analysis accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing asset data and pre-defining asset factors before the actual criticality assessment. This includes collecting baseline asset information, establishing feature sets, and preparing data structures in advance, which reduces computational complexity during real-time scoring operations.
3Reliability
If real-time asset criticality monitoring is implemented to enable proactive maintenance, then operational disruptions are reduced, but the system complexity and resource requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where the ML model continuously monitors asset criticality scores and provides real-time assessments. This feedback loop enables proactive identification of assets requiring maintenance or security attention, improving operational reliability while managing complexity through automated, continuous evaluation rather than periodic manual checks.
Solution Approach 2:
The system monitors changes in asset criticality parameters over time, detecting when assets transition between criticality levels. By tracking parameter changes rather than maintaining complex monitoring infrastructure for all possible failure modes, the system achieves improved reliability with managed complexity through focused parameter observation.
Data Source
AI summary
A method for auto-categorizing asset criticality using a machine learning (ML) technique in an industrial control network is disclosed. The method comprises selecting, via at least one processor, a plurality of asset factors associated with one or more assets of the industrial control network; assigning, via the at least one processor, a scale factor to each asset factor; creating, via the at least one processor, one or more clusters of the plurality of asset factors based at least on the scale factor; determining, via the at least one processor, centroids from each of the one or more clusters based at least on a Euclidean distance, to train a ML model; and deploying, via the at least one processor, the trained ML model comprising the one or more clusters having respective centroids determined, within the industrial control network to categorize an asset criticality for each of the one or more assets.