Malwarebytes Corporation

Sunil Mathew Thomas

Adam Hyder

Michael Graham Malone

Tina LaVonne Barfield

Jonathan Chan

Jonathan Eagan Rackley

Ravi Kalyan Namballa

Vijay Arumugam Velayutham

Motil Jayakar

Matthew English

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 computer security system includes a test management system and associated communication architecture that enables creation of customized tests of computer security application features. A server stores a test script in a custom scripting language. The test script includes a set of control statements that may be organized in a decision tree to control facilitation of the test. Clients poll the server to independently obtain and execute the control statements. Execution of the control statements control which clients participate in a test, which feature will be tested in the test, and what telemetry data will be collected from the clients to evaluate the test. The server evaluates the telemetry data to determine an outcome of the test and determines whether to further distribute or roll back the tested feature based on the test outcome. The testing can be utilized to rapidly and robustly deploy features that will enhance computer security.

Dynamic communication architecture for testing computer security application features

Client devices detect malware based on a ruleset received from a security server. To evaluate a current ruleset, an administrative client device initiates a ruleset evaluation of the malware detection ruleset. A security server partitions stored malware samples into a group of evaluation lists based on an evaluation policy. The security server then creates scanning nodes on an evaluation server according to the evaluation policy. The scanning nodes scan the malware samples of the evaluation lists using the rulesets and associate each malware sample with a rule of the ruleset based on the detections, if any. The security server analyzes the associations and optimizes the ruleset and stored malware samples. The security server sends the optimized ruleset to client devices such that they more efficiently detect malware samples.

Parallel processing for malware detection

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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Palm Harbor, FL, United States of America

Sunil Mathew Thomas