Spoofed Call Identifier Analysis and Mitigation
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Solution Overview
Problem
Call spoofing techniques allow nefarious actors to hide their actual phone number, making it difficult for service providers to block unwanted calls effectively, as the displayed phone number can be fake.
Innovation Solution
A system and method that utilize a server to analyze characteristics of a calling phone number, such as location and carrier network, to determine if it is spoofed, and take actions like blocking or permitting the call based on the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If call spoofing is used to hide the actual phone number, then the caller's privacy is protected, but the reliability of caller identification is degraded
Solution Approach 1:
The system performs preliminary analysis of calling patterns, location data, and carrier information before the call is completed, establishing a confidence score that indicates whether the caller ID is spoofed. This preliminary action allows the system to prepare mitigation strategies in advance, such as warning the called party or blocking the call entirely.
Solution Approach 2:
The service provider acts as an intermediary between the caller and called party by analyzing call data and inserting a confidence score into the caller ID information. This intermediary layer verifies the authenticity of the caller ID without preventing legitimate calls, thus maintaining communication flow while improving identification reliability.
2Measurement precision
If the service provider analyzes calling characteristics to detect spoofing, then the accuracy of spoofed call identification is improved, but the device complexity increases
Solution Approach 1:
The analysis system is segmented into multiple independent components that each evaluate specific characteristics: location data analysis, carrier network analysis, calling pattern analysis, and device type analysis. Each component generates a portion of the overall confidence score, making the complex detection task manageable and scalable.
Solution Approach 2:
The system changes the parameters being analyzed from simple phone number matching to multi-dimensional characteristics including geographic location, carrier network identity, calling behavior patterns, and device hardware identifiers. By analyzing multiple parameters simultaneously, the system achieves high detection accuracy without requiring overly complex algorithms.
3Productivity
If the service provider blocks calls with low confidence scores, then the productivity of filtering unwanted calls is improved, but the loss of legitimate calls increases
Solution Approach 1:
Instead of blocking all calls with below-threshold confidence scores, the system applies partial action by implementing graduated responses: warnings to the called party, suggested blocking options, or time-limited blocking. This partial approach filters unwanted calls effectively while allowing legitimate calls to be recovered if the called party overrides the block.
Solution Approach 2:
The system incorporates feedback loops where user responses to warnings and block suggestions are collected and used to refine future blocking decisions. If users frequently override blocks for certain numbers, the system learns to adjust its confidence thresholds or analysis parameters, reducing false positives while maintaining filtering productivity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a spoofed telephone call identifier are disclosed. In one aspect, a method includes the actions of receiving, by a first computing device, data indicating a placement of a telephone call from a second computing device to a third computing device, wherein the data includes a phone number of the second computing device. The actions further include determining characteristics of the phone number of the second computing device. The actions further include, based on the characteristics of the phone number of the second computing device, determining a likelihood that the phone number of the second computing device is spoofed. The actions further include, based on the likelihood that the phone number of the second computing device is spoofed, determining whether to transmit a notification of the telephone call to the third computing device.