Datavisor, Inc.

Olivia Wang

Fang Yu

Nicola Corradi

Alexandros Vlissidis

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting suspicious accounts. One of the methods includes identifying one or more potential clusters of malicious accounts; for each cluster, processing a collection of content associated with each account of the cluster, the processing comprising applying a plurality of models in series to determine whether the collection of content indicates a common pattern; and based on the respective determinations, classifying the accounts of each cluster as ordinary or suspicious.

Detection of common patterns in user generated content with applications in fraud detection

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting fraudulent accounts. One of the methods includes obtaining raw data from network events associated with a collection of user accounts of an online service; processing the raw data including determining a feature set and applying the feature set to generate user groups each comprising one or more user account; evaluating each user group based on feature distributions including performing one or more Major-Key-Shared (MKS) correlation calculations on pairs of features from the feature set for the group; scoring each group based on the evaluation; and identifying groups having a score that exceeds a specified threshold as fraudulent.

Detecting malicious user accounts of an online service using major-key-shared-based correlation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for fraud detection. One of the methods includes partitioning a feature space into a plurality of sub feature spaces, wherein the feature space comprises features associated with user account events for an online service; generating one or more clusters of users for each of one or more sub feature spaces; comparing a feature profile of one or more of the clusters with a global feature profile to determine features of one or more the clusters that have concentrated key values that exceed a respective threshold value; for each of the one or more clusters, scoring the cluster including aggregating the degree to which the key values for features exceed the corresponding threshold values; and based on the scores of the one or more clusters, determining one or more fraud detection actions.

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Fremont, CA, United States of America

Olivia Wang