Mobile Device Spoofed Call Detection via Network Data
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Solution Overview
Problem
Mobile devices are vulnerable to spoofed calls from third parties who disguise their identities, leading to unwanted or fraudulent communications, as existing technologies fail to effectively differentiate between genuine and spoofed calls.
Innovation Solution
Mobile devices employ a combination of network information, such as GPS location, hardware device identifiers, and IP addresses, to determine the authenticity of incoming calls, using machine learning algorithms to improve accuracy and provide users with options to manage spoofed calls, including blocking or reporting them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mobile devices accept all incoming calls without verification, then ease of operation is maintained, but reliability deteriorates due to spoofed calls from third parties
Solution Approach 1:
The system performs preliminary verification of call authenticity by comparing caller information (phone number, geographic location, device identifier) against stored contact information before presenting the call to the user. This preliminary action identifies spoofed calls in advance, allowing the system to flag or block them before they reach the user, thus improving reliability without requiring additional user effort during the actual call acceptance process.
2Reliability
If mobile devices implement call verification mechanisms, then reliability improves by identifying spoofed calls, but device complexity increases
Solution Approach 1:
The system introduces an intermediary verification layer that acts as a mediator between the incoming call and the user. This intermediary component compares caller information against stored contact data and network information (geographic location, device identifiers) to determine authenticity. By placing this verification logic in the network layer and using existing mobile device components (processor, memory, communication interfaces), the system achieves spoofed call identification without significantly increasing overall device complexity.
3Object-affected harmful factors
If mobile devices block suspicious calls automatically, then protection against fraud improves, but loss of information occurs by potentially blocking genuine calls
Solution Approach 1:
The system implements a feedback mechanism where user responses to flagged calls are captured and used to refine future verification decisions. When the system flags a call as potentially spoofed based on information comparison, it monitors user actions (whether they answer, reject, or report the call). This feedback loop allows the system to learn from actual user behavior patterns, adjusting its verification thresholds and criteria over time to reduce false positives while maintaining protection against genuine spoofed calls.
Data Source
AI summary
Methods and apparatuses for managing spoofed calls to a mobile device are described, in which the mobile device receives a call transmitted over a cellular or mobile network. The call may include a set of information associated with the network, such as a geological location of a device that generated the call, a hardware device identifier corresponding to the device, an internet protocol (IP) address associated with the device, or a combination thereof. The mobile device may determine whether the call is spoofed or genuine based on the set of information. Subsequently, the mobile device may assist a user of the mobile device to manage the call, such as blocking the call from reaching the user, informing the user that the call is spoofed, facilitating the user to report the call as spoofed to an authority and/or a service provider of the network.