Malwarebytes Corporation

Adam Hyder

Sunil Mathew Thomas

Tina LaVonne Barfield

Mayank Gupta

Ravi Kalyan Namballa

Vijay Arumugam Velayutham

Raghuram Sri Sivalanka

Matthew English

Motil Jayakar

A malware infection prediction method predicts a likelihood that a client device is to be infected with in a period of time based on state and behavior telemetry data. A malware infection prediction system receives telemetry data associated with use (i.e. behavior data) and configuration (i.e. state data) of a client device. By using a trained model, the system predicts a likelihood of the client device becoming infected within a given time frame. Based on the predicted likelihood, the system generates recommendations including recommended actions for reducing the likelihood of the client device becoming infected. The system then generates notifications including the recommendations and sends the notifications to the client device or to an administrative account associated with the client device.

Malware infection prediction and prevention

A test management system utilizes an adaptive cohort selection technique to dynamically select and update a cohort of clients for testing a feature of a computer security application. The test management system selects an initial cohort based on high level parameters of the test including the feature to be evaluated and the statistical confidence level for the outcome. During the test, the test management system obtains real-time telemetry data relevant to evaluating the test. Depending on how the test is tracking relative to the test objectives, test management system may dynamically modify the cohort by expanding the size of the cohort or changing the cohort membership.

Intelligent real-time adaptive cohort selection for testing of computer security application features

A security server assigns users sessions to a provider that provides virtual private networks. The security server trains a machine-learned model to identify a provider from a set of providers. The security server obtains connection parameters associated with a requested VPN connection. Connection parameters comprise a location of a computing device that requested the VPN connection, a time of the requested VPN connection, a current and/or historical state of VPN performance data, and user preferences. The security server applies the machine-learned model to the connection parameters to identify a provider. The security server provisions a user session based on the provider and establishes a connection through the provider.

Learning system for virtual private network orchestration, anonymization and quality of service

A system manages the rate of false positive detections of malware by controlling release of malware definition updates. The system determines a cohort of target devices for distributing an initial release of an update of malware definitions and sends the update exclusively to the target devices. The system then obtains telemetry data which include information associated with usage of the target devices following the update. The system analyzes the telemetry data for instances of false positive detections of malware arising from the update to the malware definitions. Based on the analysis of the telemetry data, the system determines whether to further distribute the update outside of the cohort of target client devices or to roll back the update provided to the cohort. The system executes the decision to further distribute the update or to roll back the update.

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Cupertino, CA, United States of America

Adam Hyder