Sophos Limited

Craig Paradis

Mehdi Karimibiuki

Kenneth D Ray

Andrew James Thomas

Karl Ackerman

John Edward Tyrone Shaw

An example method can include, obtaining information about a machine learning (ML) model configured to detect malicious activity on a computer system, wherein the information includes one or more of a model type of the ML model, an output type of the ML model, or a type of malicious activity that the model is trained to detect, receiving a training dataset, wherein the training dataset includes a plurality of unlabeled examples, generating an additional dataset based on the training dataset using a generative model, wherein the additional dataset includes a plurality of additional unlabeled examples, and, training the machine learning model to generate labels for each example in the training dataset and the additional dataset, using a combination of the training dataset and the additional dataset, wherein the training includes adjusting one or more parameters of the machine learning model based on accuracy of the generated labels.

Fuzz testing of machine learning models to detect malicious activity on a computer

Security is improved by adding a security heartbeat for and endpoint as a factor in a multi-factor authentication system. The security heartbeat may be used directly as an authentication factor, e.g., where the heartbeat provides a reliable and verifiable indication of identity, or the security heartbeat may be used as a gating input for some other verification method, e.g., where a text message with a temporary security code can only be transmitted to a user when the user's endpoint is providing a secure heartbeat.

Multi-factor authentication

Various aspects related to methods, systems, and computer readable media for automatic fuzz testing. An example method of automatic software fuzz testing can include, receiving a description of a target software application, determining, based on the description, a type of fuzzing, identifying one or more fuzzers based on the type of fuzzing, executing the one or more fuzzers on the target software application, extracting prioritized results of the executing of the one or more fuzzers, and, presenting the prioritized results.

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Bolton, MA, United States of America

Craig Paradis