Cisco Technology, Inc.

Plamen Nedeltchev

David Delano Ward

Alon Shlomo Bernstein

Hugo Latapie

Enzo Fenoglio

Manikandan Kesavan

Aamer Saeed Akhter

Mohit Agrawal

Max Pritikin

Gautam M Aggarwal

Pedro Leonardo

David Sisto Iacobacci

Carlos M Pignataro

Keith Griffin

Robert T Bell

Joseph T Friel

Parag Pritam Thakore

Paul G Bright-Thomas

Eric Chen

Helder F Antunes

Nathan Buckles

Anuj Sawani

David A Maluf

Warren Scott Wainner

Mark John Baugher

Dong Li

Balachander Chandrasekaran

Michael P O'Brien

Cheng Pan

Bruce McDougall

Deon J Chatterton

John Christopher Cottrell

Salman Asadullah

Eric Yu

John Buren Southerland

Qingliang Lin

Fangyan Ye

Xuanbei Lu

John T Manville

Luka Perkov

Tara K Thimmanaik

Disclosed herein are systems, methods, and computer-readable media for network communication within a Lunar and Interplanetary communication system. In one aspect, a length of time of line of sight occlusion associated with an object is determined based on a position of the object within its orbit. A dynamic forwarding table is generated that schedules packet paths among nodes within the network based on the length of time of the line of sight occlusion. A packet path for a packet is determined based on the dynamic forwarding table, where the packet path is based on minimizing the length of time of the line of sight occlusion associated with the object.

Scheduled FIB to account for intermittent connectivity due to orbital dynamics

In one embodiment, a monitoring service deploys a monitoring agent to a client device. The monitoring service receives certificate information for a certificate intercepted by the monitoring agent during an online transaction. The monitoring service determines, based on the certificate information, an expiration time for the certificate intercepted by the monitoring agent. The monitoring service provides an expiration notification for display, in advance of the expiration time for the certificate.

Byte code monitoring to avoid certificate-based outages

In one embodiment, an illustrative method herein may comprise: recognizing, by a device, key entries within a textual data set having a plurality of files; forming, by the device, a group of files by selecting a subset of the plurality of files that are considered by the device to be similar according to a tolerance-based similarity metric and a bi-dimensional hierarchical multiclass classification; generating, by the device, a metadata profile for the group of files by aggregating metadata from its constituent files; and causing, by the device and based on the metadata profile, a risk identification or remediation action to be performed on the textual data set.

Security audit of data-at-rest

Resource provider specifications, characterizing computing resources of computing resource providers, are received. The reachability of each IP address included in the received specification is determined. An agent is deployed that is operable to determine the value of each of a set of metrics in the environment of the host at which the agent is deployed. The agent determines the value of each metric of the set of metrics in the environment of the relevant host, and communicates the determined values to one or more computing devices that validate whether the resources characterized by the communicated values are sufficient to provide the performance characterized by the received specification and that each ISP router complies with a predetermined policy. For each computing resource provider validated and determined to comprise an ISP router compliant with policy, the specified computing resources are added to a pool of resources for cloud computing.

Crowd-sourced cloud computing resource validation

Systems and methods are disclosed for anticipating a video switch to accommodate a new speaker in a video conference comprising a real time video stream captured by a camera local to a first videoconference endpoint is analyzed according to at least one speaker anticipation model. The speaker anticipation model predicts that a new speaker is about to speak. Video of the anticipated new speaker is sent to the conferencing server in response to a request for the video on the anticipated new speaker from the conferencing server. Video of the anticipated new speaker is distributed to at least a second videoconference endpoint.

Speaker anticipation

In one embodiment, a device in a network monitors a plurality of traffic flows in the network. The device extracts a plurality of features from the monitored plurality of traffic flows. The device generates a context model by using deep learning and reinforcement learning on the plurality of features extracted from the monitored traffic flows. The device applies the context model to a particular traffic flow associated with a client, to determine a context for the particular traffic flow. The device personalizes data sent to the client from a remote source based on the determined context.

Contextual services in a network using a deep learning agent

A crowd-sourced cloud environment allows for, and benefits from, modes of interaction between among the service providers (including the 'resource providers' and the 'cloud provider') and consumers (also referred to herein as &#x201c;tenants&#x201d;) that are not practiced in a DC-centric cloud environment&#x2014;specifically, the use of Internet-based social networking technology and Internet-based online marketplace technology to facilitate resource pooling and interaction between crowd-sourced cloud resource providers, the cloud provider, and crowd-sourced cloud consumers.

Crowd-sourced cloud computing in a multiple resource provider environment

In one embodiment, a security device maintains a plurality of security enclaves for a computer network, each associated with a given level of security policies. After detecting a given device joining the computer network, the security device places the given device in a strictest security enclave of the plurality of security enclaves in response to joining the computer network. The security device then subjects the given device to joint adversarial training, where a control agent representing behavior of the given device is trained against an inciting agent, and where the inciting agent attempts to force the control agent to misbehave by applying destabilizing policies. Accordingly, the security device may determine control agent behavior during the joint adversarial training, and promotes the given device to a less strict security enclave of the plurality of enclaves in response to the control agent being robust against the attempts by the inciting agent.

Vulnerability analysis and segmentation of bring-your-own IoT devices

A method provided in a network including edge devices to collect data from data producers connected to the edge devices and to communicate with cloud-based prosumers connected with the edge devices. Data analytics tasks are identified. The data analytics tasks are used to process data collected from a data producer among the data producers to produce a result for consumption by one or more of the cloud-based prosumers. For each data analytics task it is determined whether a computational complexity of the data analytics task is less than or equal to a predetermined computational complexity. Each data analytics task determined to have a computational complexity less than or equal to the predetermined computational complexity is assigned to an edge device among the edge devices. Each data analytics task determined to have a computational complexity that exceeds the predetermined computational complexity is assigned to a prosumer among the prosumers.

Distributing data analytics in a hierarchical network based on computational complexity

In one implementation, data is communicated along a communications route in a network. A mediatrace request is generated for the communications route. Responses to the mediatrace request are received from along the communications route. The hop-by-hop latency is passively measured, from the responses, with one-way delay along the communications route in the network.

Calculating latency in computer networks

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San Jose, CA, United States of America

Plamen Nedeltchev