Google Inc.

Mandiant Corporation

Philip Tully

Michael Sikorski

Matthew Haigh

Jay Gibble

A processor-implemented method for detecting an information operations campaign includes retrieving a first neural network language model including a natural language model trained on a first dataset. The first neural network language model is modified, via transfer learning and based on a second dataset, to produce a second neural network language model. Social media post data associated with a social media post is received, and features are extracted from the social media post data. The features are tokenized to produce at least one token including a value. A prediction score for the at least one token is generated using the trained neural network language model. If the prediction score exceeds a threshold value, a threat warning including a representation associated with at least one of the social media post or an account associated with the social media post is generated.

Detecting information operations campaigns in social media with machine learning

An automated system and method for analyzing a set of extracted strings from a binary is disclosed including processing the binary with a string-extraction logic that can locate strings within the binary and output an extracted string set for use in cybersecurity analysis. The logic retrieves a set of training data comprising a plurality of previously analyzed extracted string sets where each element of the previously analyzed extracted string set comprises at least one extracted string and a corresponding previously determined threat prediction score. A prediction model based upon the training data is generated and the extracted string set is processed by the prediction model to determine a threat prediction score for each string. Ranking of the located strings is based upon the determined threat prediction score, and an output of a ranked string list is generated.

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Philip Tully