Emc IP Holding Company LLC

Liat Ben-Porat

Maya Herskovic

Christina Tkachenko

Idan Achituve

Or Navri

Rei Maoz

Tal Aboudy

Julia Petukhov

Stas Khoroshevsky

Chen Gantz

Methods, apparatus, and processor-readable storage media for user authentication with acoustic fingerprinting are provided herein. An example computer-implemented method includes generating, in response to an authentication request from a given device, an instruction for an acoustic output to be emitted and recorded by the given device; obtaining the recorded acoustic output from the given device; creating an acoustic fingerprint by applying one or more signal processing algorithms to the recorded acoustic output; processing the acoustic fingerprint and one or more items of information pertaining to the given device against historical authentication data; and resolving the authentication request in response to a determination that the acoustic fingerprint and the one or more items of information pertaining to the given device match at least a portion of the historical authentication data.

User authentication with acoustic fingerprinting

A method includes generating a static model for classifying transactions of a designated type, the static model being trained using predefined input data corresponding to a first set of features generic to transactions of the designated type, and generating a dynamic model for classifying transactions of the designated type, the dynamic model being trained using dynamic input data corresponding to a second set of features specific to subsets of transactions of the designated type. The method also includes combining the static and dynamic models to generate a combined model, detecting transactions of the designated type between client devices and an enterprise system, and utilizing the combined model to classify a given detected transaction between a given client device and the enterprise system as potentially malicious or benign. The method further includes modifying processing of the given detected transaction responsive to classifying the given detected transaction as potentially malicious.

Combining static and dynamic models for classifying transactions

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Liat Ben-Porat