Broadband Sharing Detection via ML Usage Metrics
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Solution Overview
Problem
Broadband internet service providers face significant revenue losses and security threats due to unauthorized sharing of Wi-Fi passwords, particularly in dense urban areas, which existing technologies have not adequately addressed.
Innovation Solution
A system and method using machine learning to detect broadband internet access sharing by analyzing usage data from user equipment devices, including downstream and upstream bandwidth, WAN link utilization, and signal strength, to identify suspected sharing accounts and reduce false positives through various filtering methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If broadband service providers monitor and detect Wi-Fi password sharing to protect revenue, then revenue loss is reduced, but false positives increase and legitimate users may be incorrectly flagged
Solution Approach 1:
The system changes multiple parameters simultaneously to detect sharing behavior: it monitors number of connected devices, signal strength metrics, data usage patterns, and device mobility characteristics. By analyzing combinations of these parameters rather than single metrics, the system achieves more accurate detection while reducing false positives.
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously refined. When devices are flagged as suspicious, the system gathers additional verification data and adjusts detection thresholds based on confirmed sharing cases versus false positives, improving overall detection precision over time.
2Ease of operation
If providers use simple threshold-based detection methods, then implementation is easier, but detection precision decreases and false positives increase
Solution Approach 1:
The detection system is segmented into multiple independent analysis modules: device connection analysis, signal strength analysis, data usage analysis, and mobility analysis. Each module evaluates specific parameters and produces separate scores, which are then combined to generate an overall sharing probability. This modular approach maintains implementation ease while improving precision through comprehensive analysis.
3Productivity
If providers deploy FWA services in dense urban areas to increase market share, then productivity increases, but Wi-Fi sharing risk increases leading to greater revenue loss
Solution Approach 1:
The system performs preliminary detection and analysis before significant revenue loss occurs. By continuously monitoring network parameters and identifying sharing behavior early in the deployment cycle, providers can take preventive actions such as sending warnings or adjusting service terms before substantial unauthorized usage accumulates.
Data Source
AI summary
Data describing broadband internet usage is collected from a user equipment device associated with each respective user account. The data are then input into a machine learning model that is trained on broadband internet usage data of a plurality of user accounts in a single geographic area. An n-dimensional metric for each user account is then obtained as output from the machine learning model. Using the n-dimensional metric, at least one candidate user account suspected of being engaged in broadband internet access sharing is identified, and a list of candidate user accounts is generated.