Micro Focus Lcc

Hewlett Packard Enterprise Development LP

Alexander Ulanov

Manish Marwah

Pratyusa Kumar Manadhata

Jun Li

Mijung Kim

Examples herein involve processing data in a distributed data processing system using an off-heap memory store. An example involves allocating a shared memory region of a shared memory to store attributes corresponding to a first partition of a distributed data system, and updating, in the shared memory region, the attributes corresponding to updates to the local data from process iterations of the first partition, such that a second partition of the distributed data system has access to the updated attributes.

Shared memory for distributed data

In some examples, a system receives anomaly scores regarding an entity from a plurality of detectors, produces a weighted anomaly score for the entity based on the anomaly scores and respective weights assigned to the plurality of detectors, the weights based on historical performance of the plurality of detectors, determines an impact based on a context of the entity, wherein the impact is indicative of an effect that the entity would have on a computing environment if the entity were to exhibit anomalous behavior, and computes a risk score for the entity based on the weighted anomaly score and the determined impact.

Risk scores for entities

In some examples, a system extracts features from event data representing events in a computing environment, trains ensembles of machine-learning models for respective analytics modules of a plurality of different types of analytics modules, and detects, by the different types of analytics modules using the respective trained ensembles of machine-learning models, an anomalous entity in response to further event data.

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Palo Alto, CA, United States of America

Alexander Ulanov