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公开(公告)号：US20230179616A1
公开(公告)日：2023-06-08
申请号：US17545594
申请日：2021-12-08
Applicant: L3Harris Technologies, Inc.
Inventor： Mark Rahmes , Kevin Fox , Robert Konczynski , Ziad Chaudhry , Kusay Rukieh , Jody Flieder , Macaulay Osaisai
IPC: G06N7/00
CPC classification number: H04L63/1425 , G06N7/005
Abstract: Network security anomaly detection systems and methods include a processor, in communication with the network, receiving network device status information. A variational autoencoder receives the device status information, optimizes the device status information, and determines whether the device status information qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.
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Patent Agency Ranking
公开(公告)号：US20250054159A1
公开(公告)日：2025-02-13
申请号：US18933733
申请日：2024-10-31
Applicant: L3Harris Technologies, Inc.
Inventor： Mark Rahmes , Jennifer Spicer , Robert Konczynski , Kusay Rukieh , Jody Flieder , Dustin Ellsworth , Michael Fischer , Timothy Bruce Faulkner
Abstract: Systems and methods for detecting anomalies in aviation data communication systems (e.g., air traffic control surveillance systems), include a processor receiving device status information. A variational autoencoder receives and optimizes the device status information and determines whether it qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.
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公开(公告)号：US12149550B2
公开(公告)日：2024-11-19
申请号：US17545594
申请日：2021-12-08
Applicant: L3Harris Technologies, Inc.
Inventor： Mark Rahmes , Kevin Fox , Robert Konczynski , Ziad Chaudhry , Kusay Rukieh , Jody Flieder , Macaulay Osaisai
Abstract: Network security anomaly detection systems and methods include a processor, in communication with the network, receiving network device status information. A variational autoencoder receives the device status information, optimizes the device status information, and determines whether the device status information qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.
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公开(公告)号：US20250039210A1
公开(公告)日：2025-01-30
申请号：US18912009
申请日：2024-10-10
Applicant: L3Harris Technologies, Inc.
Inventor： Mark Rahmes , Kevin Fox , Robert Konczynski , Ziad Chaudhry , Kusay Rukieh , Jody Flieder , Macaulay Osaisai
Abstract: Network security anomaly detection systems and methods include a processor, in communication with the network, receiving network device status information. A variational autoencoder receives the device status information, optimizes the device status information, and determines whether the device status information qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.
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公开(公告)号：US12190525B2
公开(公告)日：2025-01-07
申请号：US17552074
申请日：2021-12-15
Applicant: L3Harris Technologies, Inc.
Inventor： Mark Rahmes , Jennifer Spicer , Robert Konczynski , Kusay Rukieh , Jody Flieder , Dustin Ellsworth , Michael Fischer , Timothy Bruce Faulkner
Abstract: Systems and methods for detecting anomalies in aviation data communication systems (e.g., air traffic control surveillance systems), include a processor receiving device status information. A variational autoencoder receives and optimizes the device status information and determines whether it qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.
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6.发明公开公开(公告)号：US20230186482A1
公开(公告)日：2023-06-15
申请号：US17552074
申请日：2021-12-15
Applicant: L3Harris Technologies, Inc.
Inventor： Mark Rahmes , Jennifer Spicer , Robert Konczynski , Kusay Rukieh , Jody Flieder , Dustin Ellsworth , Michael Fischer , Timothy Bruce Faulkner
CPC classification number: G06T7/13 , G06V10/457 , G06V10/462 , G06V10/476 , G06V10/26 , G08G5/0004 , G08G5/0073 , G06T2207/10028
Abstract: Systems and methods for detecting anomalies in aviation data communication systems (e.g., air traffic control surveillance systems), include a processor receiving device status information. A variational autoencoder receives and optimizes the device status information and determines whether it qualifies as an anomaly. Optimized device status information is compared to either non-anomalous or anomalous device status data in a latent space of the variational autoencoder. The latent space preferably includes an n-D point scatter plot and hidden vector values. The processor optimizes the device status information by generating a plurality of probabilistic models of the device status information and determining which of the plurality of models is optimal. A game theoretic optimization is applied to the plurality of models, and the best model is used to generate the n-D point scatter plot in latent space. An image gradient sobel edge detector preprocesses the device status information prior to optimization.
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