Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In one or more embodiments, a first entity may create a profile by providing content via an interface or a first interface. A second entity may manage one or more aspects of the profile via a second interface. The amount of control the second entity has over one or more portions of the profile may be related to or based on content of the profile or a relationship between the first entity and the second entity. For example, the second entity may screen content of the profile from public view. Additionally, profiles may be searched internally within an organization or externally, such as for outsourcing or to provide vendors or clients with more customized solutions. In this manner, profile management is provided.

Abstract: 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.

Abstract: Systems and methods for device-agnostic, multi-factor network authentication are disclosed. In some embodiments, a wireless network connection can authenticate a device over secure authentication means with a certificate that confirms a device identity. After authenticating the device, a user can be prompted to provide credentials in a captive portal. The captive portal can be inaccessible to devices that have not already authenticated using a certificate. After providing approved credentials to the captive portal, the user can access the network. This embodiment and additional embodiments are readily integrated into private wireless networks and others.

Abstract: 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.

Abstract: The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of managing network entities. The innovation scans a network system to determine network entities, e.g. hardware devices and/or software applications. The innovation receives version information about hardware and software. The innovation compares version information to end-of-life information regarding the network entities. The innovation determines potential mitigating actions based on the comparison and creates an end-of-schedule for managing vendor contracts with regards to end-of-life network entities.

Abstract: The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of intelligent learning and management of networked architectures. The innovation maps a networked architecture. The networked architecture includes elements having software elements and hardware elements interconnected in a common environment. The innovation determines data sources associated with the set of elements using the identifiers. The innovation compiles data associated with the set of elements into a knowledgebase. The innovation utilizes machine learning to analyze information from the data sources. The innovation determines a configuration for at least one element in the environment based on the mapping. The innovation executes the configuration based on the configuration.

Abstract: 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.