Machine-learned model-driven anti-piracy framework for content streaming systems

A content streaming system, comprising:

• a machine-learned model;
• and
• one or more computing devices operable to:
• receive a streaming request for video content from a client device operating on a subnet;
• process, by the machine-learned model, the streaming request and a plurality of transactional logs associated with the subnet of the client device;
• determine that the subnet of the client device is a malicious subnet based on the streaming request and the plurality of transactional logs associated with the subnet of the client device, the malicious subnet being associated with hosting pirated video content;
• and
• responsive to determining that the subnet of the client device is the malicious subnet, disable video content transactions to the malicious subnet.

A non-transitory computer-readable storage medium that includes executable instructions configured to cause a processor device of a content streaming system comprising a machine-learned model to:

• receive a streaming request for video content from a client device operating on a subnet;
• process, by the machine-learned model, the streaming request and a plurality of transactional logs associated with the subnet of the client device;
• determine that the subnet of the client device is a malicious subnet based on the streaming request and the plurality of transactional logs associated with the subnet of the client device, the malicious subnet being associated with hosting pirated video content;
• and
• responsive to determining that the subnet of the client device is the malicious subnet, disable video content transactions to the malicious subnet.