Wells Fargo Bank, N.a.

Other

Lawrence T Belton, Jr

Peter Anatole Makohon

Timothy H Morris

Ryan Bernard Benskin

Christopher J Houser

Lynn Allen Smith

Omar S Bracey

Douglas C Rambo

Robert Glenn Yelton, Jr

Douglas S Rodgers

Jonathan D Russell

Jon Gabel

Robert I Kirby

Joseph R Kaluzny

Nathan Thangavadivel Suri

Marci J Alley

Marcos Bilbao

Scott Hinzman

Brian Beaty

Scott A Keoseyan

Tammy C Fleming

Jonathan A McNeill

Samantha Grosby

Deb J Sante

Jennifer Imus

Melissa Nicoletti

Jeremy B Hairston, Sr

Tyra Frison

Kristina Draper

Michael Grove

Jon Matthew Welborn

Gary Simms

Terrence W Gareau

Jacob Lee

Jonathan Louis Gabel

Doug S Rodgers

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of determining network segmentation. The innovation can search a network to determine a set of network entities, the network entities belonging to the network, and determine network factors of each network entity in the set of network entities. The innovation can evaluate each network factor and determine segmentation candidates based on the evaluation of each network factor. The innovation can determine a risk ranking for each network factor for each network entity and aggregate each risk ranking into a segmentation score for each network entity. The innovation can determine a segmentation candidate when a network entity segmentation score satisfies a threshold score. The innovation can generate a sub-network that is part of the network for the segmentation candidate, and transfer the segmentation candidate to the sub-network.

Network segmentation

Network entities of a network system are managed in an end-of-life context. A network system is scanned to determine network entities such as hardware devices and/or software applications. A network entity can be identified as vulnerable based on end-of-life data. A risk score for the vulnerable network entity is computed based on the end-of-life data and optionally other factors, and a potentially mitigating action is determined based on the risk score.

End-of-life management system

Provided are a universal software installer and/or uninstaller. The universal software installer determines a structure of the software to be installed and verifies all necessary software elements are applied or installed on the endpoint during the install. The universal software uninstaller determines a structure of the software to be uninstalled and verifies all related software elements are removed from the endpoint. The universal software installer and/or uninstaller is independent of an operating system platform executing on the endpoint.

Universal installer and uninstaller

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of autonomous asset configuration modeling and management. The innovation includes probing elements of a networked architecture to compile information about elements in the networked architecture. The innovation learns a configuration for the at least one element in the environment based on the probing and determines vulnerabilities in the learned configuration. The innovation develops a threat model based on the learned configuration. The innovation applies the threat model to the elements of the networked architecture and deploys a configuration that resolves the vulnerabilities based on the threat model to the elements in the networked architecture. The threat model can be developed over time using machine learning concepts and deep learning of data sources associated with the elements and vulnerabilities.

Autonomous configuration modeling and management

Systems and methods that facilitate operational support for network infrastructures are discussed. The disclosed system and method facilitate a unified view of the current state of the network and networked devices including real-time log monitoring and for providing metrics for long term system planning. One such method can include the acts of automatically discovering a device deployed on a network, receiving device and network related data in real-time, determining whether a device is authorized, terminating device network access, filtering device data, validating device configuration, configuring a device and providing an output for use by a user. The disclosed system and method can be utilized, for example, to reduce the time involved in troubleshooting and resolving network issues, for establishing a baseline for network performance and for network capacity planning.

Operational support for network infrastructures

Provided is predictive modeling for anti-malware solutions. A profile for a device is determined based on at least one characteristic identified from a successful attempt by the device to access a network. An expected characteristic for a next access attempt by the device to access the network is determined based on the profile. The characteristic of the next access attempt is matched to the expected characteristic. In response to determining that at least one characteristic of the next access attempt matches the expected characteristic, the next access attempt by the device to the network is automatically granted.

Predictive modeling for anti-malware solutions

Situational awareness and perimeter protection orchestration determines when network attacks are occurring, or predicts their occurrence, and provides tools and services to mitigate the attacks. The attacks can be denial of service attacks or distributed denial of service attacks or other types of attacks designed to disable and degrade a network. The dashboard can collect intelligence on what is happening on the network, and also streams of information from third parties that can be used to predict imminent network attacks. The dashboard can also determine what tools and services are available to the network operator in order to counteract the attacks.

Situational awareness and perimeter protection orchestration

Intelligent learning and management of networked architectures is disclosed. A network architecture can be mapped to identify a set of interconnected hardware and software elements that comprise the network architecture. Data sources associated with the set of interconnected hardware and software elements can be identified and employed to compile data associated with the elements. The data can be utilized to determine an action to address potential negative effects of a change to the network architecture such as an update or patch. In one instance, the action corresponds to a reconfiguration of at least one of the set of interconnected hardware and software elements. Further, machine learning can be employed to determine a particular configuration. Once determined the action can be implemented on the network architecture.

Intelligent learning and management of a networked architecture

Dynamic directory service object creation and certificate management can be performed. In response to discovering a device connected to a network, a corresponding directory service object can be automatically created, and a digital certificate can be automatically acquired and deployed on the device to facilitate authentication. Further, actions can be logged, and notifications generated based on logged actions. Time involved in deploying and configuring directory services is reduced, efficiency is improved, and there is less of a chance for errors associated with manual configuration.

Dynamic object creation and certificate management

Provided is predictive modeling for anti-malware solutions. The predictive modeling includes an identification manager component that generates profile data for a hostile source. The hostile source is identified based on a previous threat attributed to the hostile source. The predictive modeling also includes an evaluation component that determines a characteristic of an interaction between a source and an endpoint. Further, the predictive modeling includes a validation component that compares the characteristic of the interaction with the profile data and controls access to the source by the endpoint based on the comparison. In addition, anti-malware software is not deployed on the endpoint.

Growing community of inventors

Charlotte, NC, United States of America

Lawrence T Belton, Jr