Access detection method, system, and apparatus

A computing device, comprising a memory, a processor, and computer instructions stored in the memory and executable by the processor, wherein when the computer instructions, when executed by the processor, cause the processor to implement operations comprising:

• obtaining user attribution information of a target object in a predetermined duration of time from network-based user activity data, wherein the user activity data includes machine-generated telemetry comprising freezing reports generated by client devices during content access;
• processing the user attribution information to determine attribution types;
• determining a distribution of users accessing the target object in the predetermined duration of time based on the attribution types and the user attribution information;
• determining a distribution of users with freezing reporting about the target object in the predetermined duration of time based on the attribution types and the user attribution information;
• calculating a distribution of freezing rates in the predetermined duration of time based on the distribution of users accessing the target object and the distribution of users with freezing reporting about the target object;
• determining based on the distribution of freezing rates, whether abnormal access indicative of fraudulent activity exists;
• and
• automatically initiating a system-level action to manage abnormal access conditions in response to determining that the abnormal access indicative of fraudulent activity exists.

A non-transitory computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and wherein the computer instructions, when executed by a processor, cause the processor to implement operations comprising:

• obtaining user attribution information of a target object in a predetermined duration of time from network-based user activity data, the user activity data including machine-generated telemetry comprising freezing reports generated by client devices during content access;
• processing the user attribution information to determine attribution types;
• determining a distribution of users accessing the target object in the predetermined duration of time based on the attribution types and the user attribution information;
• determining a distribution of users with freezing reporting about the target object in the predetermined duration of time based on the attribution types and the user attribution information;
• calculating a distribution of freezing rates in the predetermined duration of time based on the distribution of users accessing the target object and the distribution of users with freezing reporting about the target object;
• and
• determining based on the distribution of freezing rates, whether abnormal access indicative of fraudulent activity exists;
• and
• automatically initiating a system-level action to manage abnormal access conditions in response to determining that the abnormal access indicative of fraudulent activity exists.