Amazon Technologies, Inc.

Christopher Miller

Brendan Cruz Colon

Matthew Michael Sommer

Alexander Noble Adkins

Adam Edward Powers

Joshua Hansen

Dennis Naylor Brown

Manraj Tatla

Michael Chad McClure

Kimberly A Young

Daniel Azuara

Michael Alexander Cecil

Diana Keller

Samantha Felice

Joel Booker

Dorion Carroll

Lance Dennis Leishman

A method includes generating a set of training data, wherein a first training instance of the set of training data comprises a first plurality of messages between a first customer service agent and a first purported customer and a first label indicating whether one or more messages of the first plurality of messages are associated with malicious behavior; training a large language model (LLM) using the set of training data to generate messages; generating, by the LLM representing a second purported customer, a message associated with malicious behavior; receiving a response message from a second customer service agent based on the message associated with malicious behavior; and in response to the response message being an authorization, generating a feedback for the second customer service agent based on a number of responses before the response was an action.

Large language model to detect and emulate malicious activity

Devices and techniques are generally described for unused identity and access management rights detection. In various examples, a first skill-usage vector associated with a first profile may be determined. A first nearest neighbor algorithm and the first skill-usage vector may be used to determine a second skill-usage vector grouped together with the first skill-usage vector in a feature space, where the second skill-usage vector is associated with a second profile. A first rights vector associated with the first profile may be determined. The first rights vector associated with the first profile may be compared to a second rights vector associated with the second profile. At least one unused right associated with the first profile may be determined based at least in part on the comparing of the first rights vector to the second rights vector.

Peer-based inference of unused identity and access management rights

Devices and techniques are generally described for peer-based anomalous rights detection. In various examples, a rights vector may be determined for a first individual, the rights vector representing rights held by the first individual. A nearest neighbor algorithm may be used to determine a set of individuals having similar rights to the first individual. In various examples, a category label associated with the first individual may be determined. In some examples, a number of individuals of the set of individuals having the category label may be determined. In some examples, a determination may be made that the rights held by the first individual are anomalous based at least in part on the number. In some cases, alert data indicating that the rights held by the first individual are anomalous may be generated.

Peer-based, angular distance detection of anomalous rights

Devices and techniques are generally described for anomalous computer activity detection. In various examples, first computer activity data associated with a first account may be determined. A first linear detection event that corresponds to the first computer activity data may be determined. In some examples, a set of gradient-based data associated with the first linear detection event may be determined. The set of gradient-based data may represent comparative analysis of the first computer activity data with computer activity data of other accounts. In some examples, first data representing the first linear detection event and the set of gradient-based data may be generated. In various cases, network access for the first account may be disabled based on the first data.

Anomalous computer activity detection and prevention

Devices and techniques are generally described for fraud detection. In various examples, first data may be received from a remote computing device, the first data specifying at least one of a first internet protocol (IP) address or a first telephone number by a fraud detection service using an application programming interface (API) of the fraud detection service. In some examples, a first machine learning model of the fraud detection service may determine a first confidence score indicating a likelihood that at least one of the first telephone number or the first IP address is associated with fraudulent activity. In some examples, output data may be sent to the first remote computing device via the API, the output data indicating a determination as to whether at least one of the first IP address or the first telephone number is associated with fraudulent activity.

Click-to-call fraud detection

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Seattle, WA, United States of America

Christopher Miller