Cisco Technology, Inc.

M David Hanes

Gonzalo A Salgueiro

Charles Calvin Byers

Joseph Michael Clarke

Santosh Ramrao Patil

Derek William Engi

Jerome Henry

Robert Edgar Barton

Sebastian Jeuk

Nikhil Sainath Kale

Akramsheriff Ismailsheriff

Ana Peric

Md Atiqur Rahman

Bradley Wise

Vivek Kumar Singh

Joel Richard Goergen

Blake Harrell Anderson

Brian Eliot Weis

Akram Ismail Sheriff

Herbert Michael Wildfeuer

Shankar Ramanathan

James C Frauenthal

Dmitri Goloubev

Brooks Stevens Read

Muhilan Natarajan

Volodymyr Iashyn

Panagiotis Theodorou Kampanakis

Oliver William Fagan

Robert Thomas Taylor

Ana Perić

Michael P Obrien

Mateusz Olszowy

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.

Artificial intelligence (AI) confidentiality proxy to secure AI data sets

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.

Context injection for improved AI response

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.

Exchange engine for secure access service edge (SASE) provider roaming

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.

Automated generation of standard network device configurations

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.

Internet of things security system

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.

Intelligent and secure packet captures for cloud solutions

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.

Dynamic orchestration of machine learning functions on a distributed network

Internet of Things security system

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. The machine learning structure controller can determine whether to deploy another machine learning model architecture to the network edge devices based on the performance reports and subsequently deploy the another architecture to the network edge devices if it is determined to deploy the architecture based on the performance reports.

IoT fog as distributed machine learning structure search platform

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.

Environment aware node redundancy and optimized roaming

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.

Selective tracking of acknowledgments to improve network device buffer utilization and traffic shaping

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. The controller can adjust, based on the congestion signatures, traffic shaping operations of the plurality of network devices.

Traffic class-specific congestion signatures for improving traffic shaping and other network operations

In one example, a logical representation of a first graph is generated. The first graph indicates a configuration of a network device in a network at a first time. The first graph includes a first node representative of a first configuration block of the network device, a second node representative of a second configuration block of the network device, and a first link that indicates, by connecting the first node and the second node, that the first configuration block is associated with the second configuration block. The logical representation of the first graph is compared to a logical representation of a second graph that indicates an actual or planned configuration of the network device at a second time subsequent to the first time. In response, one or more changes in the configuration of the network device from the first time to the second time are identified.

Identification of network device configuration changes

A method includes obtaining performance characterization values from endpoints managed by a first fog node at a first hierarchical level in a hierarchy of fog nodes. The method includes changing a first operating characteristic of the wireless network based on the performance characterization values. The first operating characteristic affects the operation of one or more of the endpoints. The method includes transmitting a portion of the performance characterization values to a second fog node at a second hierarchical level in the hierarchy of fog nodes. The method includes changing a second operating characteristic of the wireless network based on an instruction from the second fog node. The second operating characteristic affects the operation of the first fog node and/or other fog nodes at the first hierarchical level. Changing one or more of the first operating characteristic and the second operating characteristic satisfies an operating threshold for the wireless network.

Growing community of inventors

Lewisville, NC, United States of America

M David Hanes