Subscriber Anomaly Detection System for Unauthorized Content Distribution
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Solution Overview
Problem
Current technologies fail to effectively detect and prevent unauthorized distribution of subscribed content by subscribers, as they lack robust mechanisms to identify and penalize users who stream content without proper authorization.
Innovation Solution
A system comprising at least one processor that obtains subscriber data, detects anomalous patterns, identifies suspected subscribers, and implements policies to confirm or penalize unauthorized distribution, using a combination of consumption data, network data, and verification techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection methods are used to identify unauthorized distribution, then implementation is simple, but detection effectiveness is insufficient
Solution Approach 1:
The detection system is segmented into multiple independent modules: data collection module that gathers consumption and network data, anomaly detection module that analyzes patterns, verification module that confirms unauthorized distribution, and penalty module that enforces policies. Each module performs a specific function, improving detection effectiveness while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system implements feedback mechanisms where detection results are used to refine future detections. The anomaly detection module continuously learns from detected patterns, and verification results feed back into improving the accuracy of anomaly detection, creating a self-improving system that enhances reliability over time.
2Measurement precision
If comprehensive subscriber data analysis is performed to detect anomalies, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing subscriber data in the background before anomaly detection is needed. Consumption data and network data are gathered and organized in advance, so when anomaly detection is triggered, the analysis can be performed quickly on pre-processed data, improving both accuracy and reducing processing time.
Solution Approach 2:
The system applies partial action by focusing anomaly detection on specific suspicious patterns rather than analyzing all subscriber data equally. The verification module selectively investigates only those cases where anomaly detection indicates potential unauthorized distribution, avoiding unnecessary processing time while maintaining high detection accuracy for actual threats.
3Speed
If real-time monitoring of subscriber activity is implemented, then unauthorized distribution is detected faster, but system resource consumption increases
Solution Approach 1:
The system implements periodic action by monitoring subscriber activity at strategically determined intervals rather than continuously. The data collection module gathers consumption and network data periodically, and anomaly detection is triggered at specific intervals or when threshold conditions are met, enabling fast detection of unauthorized distribution while reducing system resource consumption compared to continuous real-time monitoring.
Data Source
AI summary
In one embodiment, a method performed by a system that includes at least one processor, the method comprising: obtaining subscriber data of a plurality of subscribers, wherein said subscriber data comprises at least one of: consumption data relating to subscribed content consumption by said plurality of subscribers, or network data relating to data transmittal via one or more computer networks by the plurality of subscribers; detecting anomalous data by comparing subscriber data of different subscribers in the plurality of subscribers; identifying one or more suspected subscribers out of the plurality of subscribers as being suspected of unauthorized subscribed content distribution, the one of more suspected subscribers being associated with the anomalous data; and providing a respective identity for the one or more suspected subscribers.