SYSTEM AND METHOD FOR AUTO-CATEGORIZING ASSET CRITICALITY USING MACHINE LEARNING TECHNIQUE IN INDUSTRIAL CONTROL NETWORK
卷宗概要
发明人
Harshal Haridas; Alex Zelichenko
IPC 分类
CPC 分类
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.
原文（中文）
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.