Intel Corporation

Amar Srivastava

Kshitij Arun Doshi

Christian Maciocco

Francesc Guim Bernat

Nageen Himayat

Satish Chandra Jha

Ignacio Javier Alvarez

Srikathyayani Srikanteswara

S M Iftekharul Alam

Alexander Bachmutsky

Maruti Gupta Hyde

Vesh Raj Sharma Banjade

Ravikumar Balakrishnan

Yi Zhang

System and techniques for semantic network data correction are described herein. Semantic data corresponding to a data stream may be received. Here, the semantic data is based on the data stream. The data stream, including packets, is received. At least one packet has an error due to transmission via a network link. This error introduces an ambiguity for content of the packet. The error is corrected using the semantic data. The semantic data providing a constraint on the ambiguity to eliminate possible corrections for the error.

Semantic network data correction

Systems and techniques for cross-layer automated fault tracking and anomaly detection are described herein. Anomaly data may be obtained from a plurality of layers of a network. Elements of the anomaly data may be identified that correspond to a data flow of an application executing on the network. An artificial intelligence model may be trained using the elements of the anomaly data to generate an impact score for the application. The impact score may be generated for the application by evaluating current network metrics using the artificial intelligence model. An operational component of the network may be modified based on the impact score.

Cross-layer automated fault tracking and anomaly detection

Disclosed herein are systems and methods for peer-assisted safety models for autonomous and assisted-driving vehicles. In an embodiment, a safety-model service receives a safety-model request for a safety model from a target vehicle. The safety-model service identifies, responsive to receiving the safety-model request, one or more source vehicles as safety-model input sources. The safety-model service receives safety-model data associated with the identified one or more source vehicles. The safety-model service generates, based on the safety-model request and the received safety-model data, a target-vehicle safety model for the target vehicle. The safety-model service transmits the target-vehicle safety model to the target vehicle for use by the target vehicle.

Systems and methods for brokering peer-assisted safety models for autonomous and assisted-driving vehicles

An apparatus and system to provide separate network slices for security events are described. A dedicated secure network slice is provided for PDP data from a UE. The network slice is used for detecting security issues and sending security-related information to clients. The communications in the dedicated network slice are associated with a special PDP context used by the UE to interface with the network slice. Once the UE has detected a security issue or has been notified of the security issue on the network or remote servers, the UE uses a special PDP service, and is able to stop uplink/downlink channels, close running applications and enter into a safe mode, cut off connections to the networks, and try to determine alternate available connectivity.

Separate network slicing for security events propagation across layers on special packet data protocol context

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Bengaluru, India

Amar Srivastava