Patents by Inventor John Steven Mancini

John Steven Mancini has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11973768
    Abstract: Disclosed is an improved method, system, and computer program product for identifying malicious payloads. The disclosed approach identifies potentially malicious payload exchanges which may be associated with payload injection or root-kit magic key usage.
    Type: Grant
    Filed: November 24, 2020
    Date of Patent: April 30, 2024
    Assignee: Vectra AI, Inc.
    Inventors: Nicolas Beauchesne, John Steven Mancini
  • Patent number: 11595416
    Abstract: Disclosed is an improved approach for identifying security risks and breaches in a network by applying machine learning methods that learn resource access patterns in the network. Specifically, by observing the access pattern of the network entities (e.g. accounts, services, and hosts) from authorization requests/responses, the model through unsupervised learning, organizes the entity relationships into an ensemble of hierarchical models. The ensemble of hierarchical models can then be leveraged to create a series of metrics that can be used to identify various types of abnormalities in the access of a resource on the network. For instance, by further classifying the access request for a resource using abnormality scores into detection scenarios, the model is able to detect both an abnormality and the type of abnormality and include such information in a corresponding alarm when a security breach happens.
    Type: Grant
    Filed: April 28, 2020
    Date of Patent: February 28, 2023
    Assignee: Vectra AI, Inc.
    Inventors: Hsin Chen, Nicolas Beauchesne, Himanshu Mhatre, John Steven Mancini
  • Publication number: 20210105290
    Abstract: Disclosed is an improved method, system, and computer program product for identifying malicious payloads. The disclosed approach identifies potentially malicious payload exchanges which may be associated with payload injection or root-kit magic key usage.
    Type: Application
    Filed: November 24, 2020
    Publication date: April 8, 2021
    Applicant: Vectra AI, Inc.
    Inventors: Nicolas Beauchesne, John Steven Mancini
  • Publication number: 20200374308
    Abstract: Disclosed is an improved approach for identifying security risks and breaches in a network by applying machine learning methods that learn resource access patterns in the network. Specifically, by observing the access pattern of the network entities (e.g. accounts, services, and hosts) from authorization requests/responses, the model through unsupervised learning, organizes the entity relationships into an ensemble of hierarchical models. The ensemble of hierarchical models can then be leveraged to create a series of metrics that can be used to identify various types of abnormalities in the access of a resource on the network. For instance, by further classifying the access request for a resource using abnormality scores into detection scenarios, the model is able to detect both an abnormality and the type of abnormality and include such information in a corresponding alarm when a security breach happens.
    Type: Application
    Filed: April 28, 2020
    Publication date: November 26, 2020
    Applicant: Vectra AI, Inc.
    Inventors: Hsin Chen, Nicolas Beauchesne, Himanshu Mhatre, John Steven Mancini
  • Publication number: 20180077178
    Abstract: Disclosed is an improved method, system, and computer program product for identifying malicious payloads. The disclosed approach identifies potentially malicious payload exchanges which may be associated with payload injection or root-kit magic key usage.
    Type: Application
    Filed: September 12, 2017
    Publication date: March 15, 2018
    Applicant: Vectra Networks, Inc.
    Inventors: Nicolas Beauchesne, John Steven Mancini