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公开(公告)号：US11625483B2
公开(公告)日：2023-04-11
申请号：US16887623
申请日：2020-05-29
Applicant: General Electric Company
Inventor： Johan Reimann , Nurali Virani , Naresh Iyer , Zhaoyuan Yang
Abstract: A system and method including receiving a set of deep neural networks (DNN) including DNNs trained with an embedded trojan and DNNs trained without any embedded trojan, each of the trained DNNs being represented by a mathematical formulation learned by the DNNs and expressing a relationship between an input of the DNNs and an output of the DNNs; extracting at least one characteristic feature from the mathematical formulation of each of the trained DNNs; statistically analyzing the at least one characteristic feature to determine whether there is a difference between the DNNs trained with the embedded trojan and the DNNs trained without any embedded trojan; generating, in response to the determination indicating there is a difference, a detector model to execute the statistical analyzing on deep neural networks; and storing a file including the generated detector model in a memory device.
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公开(公告)号：US12034741B2
公开(公告)日：2024-07-09
申请号：US17236638
申请日：2021-04-21
Applicant: General Electric Company
Inventor： Weizhong Yan , Zhaoyuan Yang , Masoud Abbaszadeh , Yuh-Shyang Wang , Fernando Javier D'Amato , Hema Kumari Achanta
CPC classification number: H04L63/1416 , G06N5/04 , G06N20/00 , G06F21/55 , H04L63/1425 , H04L63/1441 , H04L63/20
Abstract: A method for detecting a cyberattack on a control system of a wind turbine includes providing a plurality of classification models of the control system. The method also includes receiving, via each of the plurality of classification models, a time series of operating data from one or more monitoring nodes of the wind turbine. The method further includes extracting, via the plurality of classification models, a plurality of features using the time series of operating data. Each of the plurality of features is a mathematical characterization of the time series of operating data. Moreover, the method includes generating an output from each of the plurality of classification models and determining, using a decision fusion module, a probability of the cyberattack occurring on the control system based on a combination of the outputs. Thus, the method includes implementing a control action when the probability exceeds a probability threshold.
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公开(公告)号：US20220345468A1
公开(公告)日：2022-10-27
申请号：US17236638
申请日：2021-04-21
Applicant: General Electric Company
Inventor： Weizhong Yan , Zhaoyuan Yang , Masoud Abbaszadeh , Yuh-Shyang Wang , Fernando Javier D'Amato , Hema Kumari Achanta
Abstract: A method for detecting a cyberattack on a control system of a wind turbine includes providing a plurality of classification models of the control system. The method also includes receiving, via each of the plurality of classification models, a time series of operating data from one or more monitoring nodes of the wind turbine. The method further includes extracting, via the plurality of classification models, a plurality of features using the time series of operating data. Each of the plurality of features is a mathematical characterization of the time series of operating data. Moreover, the method includes generating an output from each of the plurality of classification models and determining, using a decision fusion module, a probability of the cyberattack occurring on the control system based on a combination of the outputs. Thus, the method includes implementing a control action when the probability exceeds a probability threshold.
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