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US 2022/0070183 A1Published application
Detecting malicious mobile applications using machine learning in a cloud-based system
AssigneeZscaler, Inc.
InventorRohit Goyal
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AssigneeZscaler, Inc.
InventorRohit Goyal
Estimating the term…
Systems and methods for detecting malicious mobile applications using machine learning in a cloud-based system utilize a traffic forwarding technique and a cloud-based Machine Learning (ML) model to assess the security of apps installed on a user device. This architecture enables the cloud-based system to have visibility of user devices, train the ML model in real-time with a vast amount of mobile app data from multiple users, and enforce security on the user devices from the cloud-based system. Advantageously, the ML model is trained with a vast amount of mobile traffic, leading to better accuracy of prediction. The cloud-based system can be multi-tenant (enterprise), have a large user base, be spread over a large geographic area, etc. This provides a great opportunity for training data. Feedback from live production data can be fed back into the ML model.
A non-transitory computer-readable medium storing computer-executable instructions, and in response to execution by a node in a cloud-based system, the computer-executable instructions cause the node to perform steps of:
A cloud-based system comprising:
A method comprising:
Dependencies are inferred from the claim text.