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    Memristor based storage of asset events
    3.
    发明授权
    Memristor based storage of asset events 有权

    公开(公告)号：US10922599B2

    公开(公告)日：2021-02-16

    申请号：US16220244

    申请日：2018-12-14

    Applicant: Hewlett Packard Enterprise Development LP

    Inventor： Sathyanarayanan Manamohan

    IPC: G06K19/07 ,  G11C11/21 ,  G06F9/38 ,  G06K19/077

    Abstract: An example device comprising contactless circuitry to receive data about a plurality of events corresponding to an asset, and a memristor coupled to the contactless circuitry to store the data about the plurality of events. The contactless circuitry may determine that the asset has experienced an event, receive a transaction corresponding to the event from a decentralized entity, generate a hash of the transaction including a device identifier of the contactless circuitry and the transaction received from the decentralized entity, verify the hashed transaction with the decentralized entity, and store the verified hashed transaction on the memristor of the contactless circuitry, wherein the stored verified hash includes information about the event.

    PLATFORM FOR PRIVACY PRESERVING DECENTRALIZED LEARNING AND NETWORK EVENT MONITORING
    6.
    发明申请
    PLATFORM FOR PRIVACY PRESERVING DECENTRALIZED LEARNING AND NETWORK EVENT MONITORING 有权

    公开(公告)号：US20230130705A1

    公开(公告)日：2023-04-27

    申请号：US17512609

    申请日：2021-10-27

    Applicant: Hewlett Packard Enterprise Development LP

    Inventor： Madhusoodhana Chari Sesha ,  Krishna Prasad Lingadahalli Shastry ,  Sathyanarayanan Manamohan

    IPC: H04L29/06

    Abstract: Systems and methods are provided for implementing pattern detection as a first step for security improvements of a computer network. The pattern detection may utilize a machine learning (ML) model for predicting network tuple parameters. The ML model can be trained on labelled data flow information and deployed by a central server for preventing network-wide cyber-security challenges (e.g., including DNS flux, etc.). Networking devices (e.g. switches, etc.) can monitor the data flow traffic that it receives from the networking devices and classify network tuple parameters based on the flow behavior. The system can compare the output of the ML model (e.g., a classification of the data flow traffic, etc.) to an implicit label (e.g., the network tuple parameter included with the data flow traffic, etc.). When the classification matches a particular network tuple parameter, the system can generate an alert and/or otherwise identify potential network intrusions and other abnormalities.

    Memristor based storage of asset events
    7.
    发明授权
    Memristor based storage of asset events 有权

    公开(公告)号：US11455504B2

    公开(公告)日：2022-09-27

    申请号：US17167278

    申请日：2021-02-04

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Sathyanarayanan Manamohan

    IPC: G06K19/07 ,  G11C11/21 ,  G06F9/38 ,  G06K19/077

    Abstract: An example device comprising contactless circuitry to receive data about a plurality of events corresponding to an asset, and a memristor coupled to the contactless circuitry to store the data about the plurality of events. The contactless circuitry may determine that the asset has experienced an event, receive a transaction corresponding to the event from a decentralized entity, generate a hash of the transaction including a device identifier of the contactless circuitry and the transaction received from the decentralized entity, verify the hashed transaction with the decentralized entity, and store the verified hashed transaction on the memristor of the contactless circuitry, wherein the stored verified hash includes information about the event.

    Platform for privacy preserving decentralized learning and network event monitoring
    8.
    发明授权
    Platform for privacy preserving decentralized learning and network event monitoring 有权

    公开(公告)号：US12088610B2

    公开(公告)日：2024-09-10

    申请号：US17512609

    申请日：2021-10-27

    Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

    Inventor： Madhusoodhana Chari Sesha ,  Krishna Prasad Lingadahalli Shastry ,  Sathyanarayanan Manamohan

    IPC: H04L9/40

    CPC classification number: H04L63/1425 ,  H04L63/145 ,  H04L63/1466 ,  H04L63/1483

    Abstract: Systems and methods are provided for implementing pattern detection as a first step for security improvements of a computer network. The pattern detection may utilize a machine learning (ML) model for predicting network tuple parameters. The ML model can be trained on labelled data flow information and deployed by a central server for preventing network-wide cyber-security challenges (e.g., including DNS flux, etc.). Networking devices (e.g. switches, etc.) can monitor the data flow traffic that it receives from the networking devices and classify network tuple parameters based on the flow behavior. The system can compare the output of the ML model (e.g., a classification of the data flow traffic, etc.) to an implicit label (e.g., the network tuple parameter included with the data flow traffic, etc.). When the classification matches a particular network tuple parameter, the system can generate an alert and/or otherwise identify potential network intrusions and other abnormalities.

    System and method of decentralized machine learning using blockchain
    9.
    发明授权
    System and method of decentralized machine learning using blockchain 有权

    公开(公告)号：US11605013B2

    公开(公告)日：2023-03-14

    申请号：US16163159

    申请日：2018-10-17

    Applicant: Hewlett Packard Enterprise Development LP

    Inventor： Sathyanarayanan Manamohan ,  Krishnaprasad Lingadahalli Shastry ,  Vishesh Garg

    IPC: G06N5/04 ,  G06N99/00 ,  G06K9/62 ,  H04L9/06 ,  G06N5/043 ,  G06N20/00 ,  G06F18/214 ,  H04L9/00

    Abstract: Decentralized machine learning to build models is performed at nodes where local training datasets are generated. A blockchain platform may be used to coordinate decentralized machine learning over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes. Rules in the form of smart contracts may enforce node participation in an iteration of model building and parameter sharing, as well as provide logic for electing a node that serves as a master node for the iteration. The master node obtains model parameters from the nodes and generates final parameters based on the obtained parameters. The master node may write its state to the distributed ledger indicating that the final parameters are available. Each node, via its copy of the distributed ledger, may discover the master node's state and obtain and apply the final parameters to its local model, thereby learning from other nodes.

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