Dell Products Inc.

Dell & Products L.p.

Isabella Costa Maia

Karen Stéfany Martins

Paulo Abelha Ferreira

Pablo Nascimento Da Silva

Werner Spolidoro Freund

Julia Drummond Noce

Adriana Bechara Prado

Alexander Eulalio Robles Robles

Eduarda Tatiane Chagas

One example method may be performed in a distributed environment that includes a group of nodes configured to communicate with each other, and to communicate with a central node of the distributed environment. The example method includes performing, by two or more of the nodes, the operations of generating a public/private key pair, generating a secret shared key using the respective public keys of the other nodes, using the secret shared key to generate isometric transformations of data of the node, and transmitting the transformed data to the central node.

Non-anonymized privacy preserving global and local anomaly detection in distributed systems

One example method includes deploying a malware detection model in a production environment, performing a monitoring process that comprises capturing data from the production environment, by the malware detection model, determining, by the malware detection model, that a likelihood that the data belongs to a domain known to the malware detection model falls below a threshold, determining, by the malware detection model, whether or not the data is noise, or comes from a new domain not known to the malware detection model, and when it is determined that the data comes from the new domain, adapting the malware detection model by incorporating knowledge about the new domain in the malware detection model so that the malware detection model is operable to detect malware in the new domain, as well as the known domain.

Continuously propagating knowledge to decipher unknown malware in zero-trust architectures

Techniques are disclosed for hybrid machine learning model training and deployment to mobile edge devices. An example system includes a processor communicatively coupled to a memory and configured to execute instructions. Example instructions include receiving geolocation data from mobile edge devices; using the geolocation data to index the mobile edge devices into sub-regions; ranking associated machine learning (ML) models in the sub-regions based on performance metrics; and using a current geolocation of a particular mobile edge device within a given sub-region to serve a selected ML model to the particular device, wherein the ML model to serve is selected based on the performance metrics of the selected ML model within the given sub-region.

Hybrid machine learning model training and deployment to mobile edge devices

Growing community of inventors

São Paulo, Brazil

Isabella Costa Maia