Machine Learning Detection and Reconfiguration for Phantom Cellular Calls
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Solution Overview
Problem
Cellular networks face a significant challenge with phantom calls, particularly phantom E911 calls, which consume critical resources and are difficult to detect and classify due to varying device configurations and network complexities, often originating from devices and network anomalies.
Innovation Solution
A machine learning model is trained using device and network level data to identify devices likely to engage in phantom calls, and these devices are reconfigured to prevent future unwanted network activity through software updates or setting modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using device and network level data to identify phantom calling devices, then the accuracy of phantom call detection is improved, but the data processing complexity and computational resources required increase
Solution Approach 1:
The patent segments the data processing task by separating device-level data (IMEI, IMSI, call detail records) from network-level data (MME, HSS, EPC information). The machine learning model processes these segmented data types independently before integrating them for final classification, reducing overall computational complexity while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary data cleaning, filtering, and feature extraction before training the machine learning model. By pre-processing the data to remove redundant information and normalize formats, the patent reduces the computational burden during model training and execution, thereby lowering data processing complexity.
2Difficulty of detecting and measuring
If network operators implement comprehensive monitoring and analysis of call detail records to detect phantom calls, then the ability to identify unwanted network activity is improved, but the network infrastructure complexity and operational overhead increase
Solution Approach 1:
The patent implements a multi-functional monitoring system that simultaneously performs call detail record collection, anomaly detection, device profiling, and network resource optimization. By making the monitoring infrastructure serve multiple purposes, the patent reduces the need for separate dedicated systems, thereby lowering overall infrastructure complexity.
Solution Approach 2:
The system employs self-service mechanisms where the machine learning model continuously learns from new data patterns and automatically updates its detection algorithms. This self-learning capability reduces the need for manual system configuration and retraining, thereby reducing operational overhead while maintaining high detection accuracy.
3Reliability
If devices are reconfigured through software updates or setting modifications to prevent phantom calls, then the effectiveness of phantom call prevention is improved, but the device management complexity and user impact increase
Solution Approach 1:
The patent applies localized reconfiguration measures tailored to specific devices or device types that exhibit phantom calling behavior. Rather than implementing blanket restrictions across all devices, the system identifies and applies targeted settings modifications only to affected devices, thereby minimizing overall device management complexity and reducing user impact.
Solution Approach 2:
The system implements feedback loops where devices that undergo reconfiguration are monitored to verify that phantom calls are successfully prevented. The results of this monitoring feed back into the machine learning model to refine future detection and prevention strategies, improving prevention effectiveness while allowing adaptive, data-driven device management.
Data Source
AI summary
The described technology is generally directed towards reducing unwanted cellular network activities, such as phantom 911 calls or other unwanted cellular network activities. Machine learning models described herein can be trained, using device level data and network level data, to identify devices that are likely to engage in an unwanted cellular network activity. A trained machine learning model can be deployed to identify devices, and devices identified by the trained machine learning model can be re-configured to prevent them from engaging in the unwanted cellular network activity. Devices likely to engage in the unwanted cellular network activity are thus identified and reconfigured to prevent future unwanted cellular network activity before it occurs.