Symantec Corporation

Chetan Verma

Michael Andrew Hart

Aleatha Parker-Wood

Darren Shou

A method for applying personalized machine learning models is provided. The method includes producing one or more feature vectors that represents features of one of a plurality of files of a file system and selecting, from a plurality of personalized machine learning models that model user accesses to the files of the file system a subset of the personalized machine learning models each of which has a plurality of non-zero weights corresponding to non-zero features of the one or more feature vectors. The method includes determining from the subset of personalized machine learning models which users of a plurality of users of the file system are likely to access the one of the plurality of files.

Method to efficiently apply personalized machine learning models by selecting models using active instance attributes

A computer-implemented method for managing access may include (1) identifying an attempt to perform, within a computing environment, an action that involves a specific entity, (2) determining that the attempted action is anomalous for the specific entity, (3) identifying a quota of allowed anomalous actions for the specific entity, (4) determining that the attempted action causes a count of anomalous actions to exceed the quota of allowed anomalous actions, and (5) performing a security action based on the determination that the attempted action causes the count of anomalous actions to exceed the quota of allowed anomalous actions. Various other methods, systems, and computer-readable media are also disclosed.

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Sunnyvale, CA, United States of America

Chetan Verma