Abstract: Techniques are described herein for implementing and using a secure access service edge (SASE) exchange system to allow SASE providers to share SASE services with other providers. A SASE exchange system may be used by any number of SASE providers to support SASE roaming by user endpoints between different SASE providers. A user endpoint may use SASE roaming to access additional sets of SASE services and capabilities that cannot be provided by a home SASE provider and/or other current SASE provider(s) of the user endpoint. In some examples, a SASE exchange system may be used to transition user endpoints from one SASE provider to another. Additionally or alternatively, the SASE exchange system may determine a combination of SASE providers that can be used to provide different subsets of shared SASE services/capabilities to a user endpoint.

Abstract: Techniques are described herein for implementing and using a secure access service edge (SASE) exchange system to allow SASE providers to share SASE services with other providers. A SASE exchange system may be used by any number of SASE providers to support SASE roaming by user endpoints between different SASE providers. A user endpoint may use SASE roaming to access additional sets of SASE services and capabilities that cannot be provided by a home SASE provider and/or other current SASE provider(s) of the user endpoint. In some examples, a SASE exchange system may be used to transition user endpoints from one SASE provider to another. Additionally or alternatively, the SASE exchange system may determine a combination of SASE providers that can be used to provide different subsets of shared SASE services/capabilities to a user endpoint.

Abstract: A system and method are provided that use an intelligence model that continuously learns and identifies changes within a production computing environment and determines if adjustments/changes to be made in the production computing environment are to be validated during testing based on a set of criteria. The intelligence model determines possible adjustments in a computing environment (and their impact during testing) that have been learned from stored/accumulated data associated with a plurality of production computing environments over time.

Abstract: A method comprises: receiving a query on a topic from a user associated with user attributes indicative of a user comprehension level on the topic; providing the query to an AI model; receiving from the AI model a response to the query that has a response comprehension level on the topic that is less than the user comprehension level; iteratively adding, to the query, topically-relevant user attributes of the user attributes to produce iterative queries that increase in technical detail on the topic; providing the iterative queries to the AI model; responsive to providing the iterative queries, receiving, from the AI model, iterative responses that increase in technical detail on the topic and have response comprehension levels that increase on the topic; and determining, among the iterative responses, a final response having a response comprehension level that most nearly matches the user comprehension level.

Abstract: A method includes creating, via a server, a plurality of virtualized human personalities associated with respective human users; receiving, via the server, a cyberattack message; determining, via the server, the cyberattack message targets a human user of the respective human users; selecting, via the server, a virtualized human personality of the plurality of virtualized human personalities based on the virtualized human personality being associated with the human user targeted by the cyberattack message; and responding, via the server, to the cyberattack message using the virtualized human personality selected from the plurality of virtualized human personalities.

Abstract: Devices and methods are discussed herein to track networked electronic devices during the course of their lifecycles. Each electronic device may be provided with a “green passport” by its manufacturer which contains all relevant information concerning the operation of the device during its lifetime. When a new electronic device is coupled to a network, it may emit a uniform resource identifier (URI) which may be received by a server that may operate as a manager for the green passports of devices within its purview. The manager may download the green passport from the manufacturer's server. The manager may verify the authenticity of the URI and/or the green passport to avoid security threats. The manager may monitor the electronic device and issue notifications throughout its lifecycle. At the end-of-life of the electronic device, the manager may issue sustainable disposable information for the device.

Abstract: A system and method for an artificial intelligence (AI) confidentiality proxy that can protect a network from the transferal of sensitive AI data sets to outside AI systems. A data contract is established between the proxy and outside AI systems could function as well as user personas for additional protection. This system can also integrate with the network and provide security policy content and inspection rules.

Abstract: Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.

Abstract: In one embodiment, a device including a processor, and a memory to store data used by the processor, wherein the processor is operative to run a manufacturer usage description (MUD) controller operative to obtain a MUD profile of an Internet of Things (IoT) device from a MUD server, the MUD profile of the IoT device including: access rights of the IoT device, and any one or more of the following a default device username and/or a default device password of the IoT device, a recommended/required device password complexity of the IoT device, at least one service that should be enabled/disabled on the IoT device, and/or allowed security protocols and/or ciphers for communication to and/or from the IoT device, enforce security of the IoT device according to the MUD profile of the IoT device. Related apparatus and methods are also described.

Abstract: A packet capture operation is configured via a first computing device. The packet capture operation is configured to capture packets provided by a second computing device. The first computing device obtains an indication that a user is within a predetermined location proximity to the second computing device. The packet capture operation is initiated in response to obtaining the indication at the first computing device.

Abstract: A packet capture operation is configured via a first computing device. The packet capture operation is configured to capture packets provided by a second computing device. The first computing device obtains an indication that a user is within a predetermined location proximity to the second computing device. The packet capture operation is initiated in response to obtaining the indication at the first computing device.

Abstract: Techniques are described herein for implementing and using a secure access service edge (SASE) exchange system to allow SASE providers to share SASE services with other providers. A SASE exchange system may be used by any number of SASE providers to support SASE roaming by user endpoints between different SASE providers. A user endpoint may use SASE roaming to access additional sets of SASE services and capabilities that cannot be provided by a home SASE provider and/or other current SASE provider(s) of the user endpoint. In some examples, a SASE exchange system may be used to transition user endpoints from one SASE provider to another. Additionally or alternatively, the SASE exchange system may determine a combination of SASE providers that can be used to provide different subsets of shared SASE services/capabilities to a user endpoint.

Abstract: Techniques for orchestrating a machine learning (ML) system on a distributed network. Determined performance levels for a ML system, determined from performance data received from the distributed network, are compared to performance requirements from the ML system. An orchestration module for the ML system then determines adjustments for the ML system that will improve the performance of the ML system and executes the adjustments for the ML system.

Abstract: In one embodiment, a device including a processor, and a memory to store data used by the processor, wherein the processor is operative to run a manufacturer usage description (MUD) controller operative to obtain a MUD profile of an Internet of Things (IoT) device from a MUD server, the MUD profile of the IoT device including: access rights of the IoT device, and any one or more of the following a default device username and/or a default device password of the IoT device, a recommended/required device password complexity of the IoT device, at least one service that should be enabled/disabled on the IoT device, and/or allowed security protocols and/or ciphers for communication to and/or from the IoT device, enforce security of the IoT device according to the MUD profile of the IoT device. Related apparatus and methods are also described.

Abstract: In one embodiment, a device including a processor, and a memory to store data used by the processor, wherein the processor is operative to run a manufacturer usage description (MUD) controller operative to obtain a MUD profile of an Internet of Things (IoT) device from a MUD server, the MUD profile of the IoT device including: access rights of the IoT device, and any one or more of the following a default device username and/or a default device password of the IoT device, a recommended/required device password complexity of the IoT device, at least one service that should be enabled/disabled on the IoT device, and/or allowed security protocols and/or ciphers for communication to and/or from the IoT device, enforce security of the IoT device according to the MUD profile of the IoT device. Related apparatus and methods are also described.

Abstract: Systems, methods, and computer-readable mediums for distributing machine learning model training to network edge devices, while centrally monitoring training of the models and controlling deployment of the models. A machine learning model architecture can be generated at a machine learning structure controller. The machine learning model architecture can be deployed to network edge devices in a network environment to instantiate and train a machine learning model at the network edge devices. Performance reports indicating performance of the machine learning model at the network edge devices can be received by the machine learning structure controller from the network edge devices.

Abstract: In one embodiment, a supervisory service for a wireless network obtains frequency-time Doppler profile information for an endpoint node attached to a first access point in the wireless network. The supervisory service uses the frequency-time Doppler profile information for the endpoint node as input to a machine learning model. The machine learning model is trained to output an action for the endpoint node with respect to the wireless network. The supervisory service causes the action for the endpoint node with respect to the wireless network to be performed.

Abstract: Systems and methods provide for Selective Tracking of Acknowledgments (STACKing) to improve buffer utilization and traffic shaping for one or more network devices. A network device can identify a first flow that corresponds to a predetermined traffic class and a predetermined congestion state. The device can determine a current window size and congestion threshold of the first flow. In response to a determination to selectively track a portion of acknowledgments of the first flow, the device can track, in main memory, information of a first portion of acknowledgments of the first flow. The device can exclude, from one or more buffers, a second portion of acknowledgments of the first flow. The device can re-generate and transmit segments corresponding to the second portion of acknowledgments at a target transmission rate based on traffic shaping policies for the predetermined traffic class and congestion state.

Abstract: Systems and methods provide for generating traffic class-specific congestion signatures and other machine learning models for improving network performance. In some embodiments, a network controller can receive historical traffic data captured by a plurality of network devices within a first period of time that the network devices apply one or more traffic shaping policies for a predetermined traffic class and a predetermined congestion state. The controller can generate training data sets including flows of the historical traffic data labeled as corresponding to the predetermined traffic class and predetermined congestion state. The controller can generate, based on the training data sets, traffic class-specific congestion signatures that receive input traffic data determined to correspond to the predetermined traffic class and output an indication whether the input traffic data corresponds to the predetermined congestion state.

Abstract: Techniques described herein relate to automatically generating standard network device configurations. In one example, one or more groups of network device configuration blocks may be obtained. An analysis of the one or more groups of network device configuration blocks may be performed, including identifying respective frequencies associated with respective network device configuration blocks of the one or more groups of network device configuration blocks. Based on the respective frequencies, one or more network device configuration blocks of the one or more groups of network device configuration blocks may be automatically aggregated into a standard network device configuration.