公开(公告)号：US20230007023A1

公开(公告)日：2023-01-05

申请号：US17364614

申请日：2021-06-30

Applicant: Dropbox, Inc.

Inventor： Sarah Andrabi ,  Effi Goldstein ,  Omer Tamir ,  Boris Borshevsky

IPC: H04L29/06 ,  G06N3/08

Abstract: This disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that utilize a machine-learning model to detect mass file deletions, mass file downloads, ransomware encryptions, or other anomalous digital events within a digital-content-synchronization platform. For example, the disclosed systems can monitor digital actions executed across a digital-content-synchronization platform in real (or near-real) time and use a machine-learning model to analyze features of such digital actions to distinguish and detect anomalous actions. Upon detection, the disclosed systems can alert a client device of the anomalous actions with an explanatory rationale and, in some cases, perform (or provide options to perform) a remedial action to neutralize or contain the anomalous actions. Furthermore, the disclosed systems can also modify the machine-learning model based on interactions received from an administrator device in response to the anomalous actions.