Subscriber Data Protection via Multi-Stage Call Analysis
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Solution Overview
Problem
Current systems fail to effectively detect and protect subscriber data from unwanted calls, leading to high false positive and false negative errors, and inadequate protection against sophisticated fraudulent schemes.
Innovation Solution
A method involving a detection module that intercepts network traffic, analyzes call parameters and stream data using techniques like speech recognition, image recognition, and machine learning to identify unwanted calls, and tags subscribers to protect sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection methods are used, then the system is simple to operate, but the detection precision is low leading to high false positive and false negative errors
Solution Approach 1:
The fraud detection system is segmented into multiple independent modules: a detection module that intercepts network traffic and analyzes call parameters, a stream data analysis module that processes call stream data using speech and image recognition, and a data protection module that tags subscriber data. Each module performs a specific function, allowing the system to achieve high detection precision through specialized analysis while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system transitions from analyzing only traditional call parameters to a multi-dimensional approach that includes both call parameters and call stream data (audio, video, images). This dimensional expansion allows the detection system to examine fraudulent calls from multiple angles simultaneously, significantly improving detection precision by capturing subtle indicators that single-dimensional analysis would miss.
2Measurement precision
If comprehensive analysis of call parameters and stream data is performed, then the detection accuracy improves, but the processing time increases
Solution Approach 1:
The detection module performs preliminary analysis of call parameters immediately upon intercepting network traffic, before the call stream data is fully transmitted. This preliminary assessment allows the system to identify obvious fraudulent patterns early and apply more intensive stream data analysis only when necessary, thereby improving detection accuracy while minimizing overall processing time for clearly fraudulent or clearly legitimate calls.
Solution Approach 2:
The system implements feedback mechanisms where the results from call parameter analysis inform the subsequent stream data analysis. If the initial parameter analysis indicates low suspicion, the system can reduce the intensity of stream data processing. Conversely, if parameters suggest potential fraud, the system intensifies stream data analysis. This feedback-driven adaptive processing improves detection accuracy while optimizing processing time based on actual risk levels.
3Reliability
If subscriber data is protected through tagging and identification, then the security level improves, but the system complexity increases
Solution Approach 1:
The system introduces tags as intermediary markers that link detection results to subscriber data without requiring direct integration between the detection module and data storage systems. These tags serve as a simple yet effective mechanism to identify and protect subscriber data associated with fraudulent calls, improving security while avoiding the complexity of deep system integration. The tags act as a lightweight bridge between detection and protection functions.
Data Source
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AI summary
A method for protecting subscriber data includes intercepting network traffic associated with a call. The network traffic includes call parameters and call stream data. A first set of the call parameters is analyzed. A first probability value of the call being declared as unwanted is determined. The call stream data is analyzed to define a second set of call parameters. The first set of call parameters is reanalyzed based on the second set. A second probability value of the call being declared as unwanted is determined. A determination is made if the second probability value exceeds a second threshold value. The call is declared as unwanted, in response to determining that the second probability value exceeds the second threshold. The first and second sets of call parameters are transmitted to an application configured to protect data of a protected subscriber.