Amazon Technologies, Inc.

Joshua Hansen

Brendan Cruz Colon

Matthew Michael Sommer

Mehran Moghaddam

Tam Minh Tran

Christopher Miller

Alexander Noble Adkins

Dennis Naylor Brown

Matt Michael Sommer

Daniel Azuara

Raj K Raheja

Eduardo Torres

Dehua Huang

Jingjing Zhao

Heather Raymon

Robert Hubbard

Matthew Alexander

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 anomalous network activity detection. In various examples, first application log data comprising a plurality of computer log actions may be received. A hidden Markov model associated may generate a first hidden state vector, where a first value of the first hidden state vector is associated with anomalous activity. A first timing vector associated with a first account may be determined, wherein the timing vector indicates first time periods of typical activity and second periods of typical inactivity for the first account. A first number of elements of the first hidden state vector that have the first value and which correspond to the second time periods of the first timing vector may be determined. Network access may be disabled for the first account.

Detection of anomalous computer-implemented service activity

Barstool

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Idaho Falls, ID, United States of America

Joshua Hansen