Telecom Network Authenticity Scoring for Hijack-Resistant 2FA
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing two-factor authentication methods relying on mobile devices are vulnerable to hijacking or theft, allowing attackers to bypass security mechanisms.
Innovation Solution
A machine learning model within a telecommunication network generates an authenticity score based on network event data associated with a telephone number, providing an additional layer of security by assessing device possession and legitimacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If two-factor authentication using mobile devices is implemented, then account security is improved, but vulnerability to device hijacking or theft increases
Solution Approach 1:
The patent introduces a telecommunication network as an intermediary between the mobile device and the authentication service. The network analyzes network event data (call logs, text messages, data usage patterns) and generates an authenticity score that reflects the likelihood the device is in legitimate possession. This intermediary layer adds verification beyond simple device possession, mitigating the vulnerability to device hijacking while maintaining the convenience of mobile-based 2FA.
2Measurement precision
If network event data analysis with machine learning is added to authentication, then authenticity verification is improved, but system complexity increases
Solution Approach 1:
The patent leverages the existing telecommunication network infrastructure to perform multiple functions: traditional call routing, message transmission, and now authenticity analysis. The network utilizes already-collected network event data (call logs, messaging patterns, data usage) that would otherwise be used solely for billing and service management. By making this existing data serve the additional function of authenticity verification through machine learning, the patent improves verification accuracy without significantly increasing system complexity.
Data Source
AI summary
A processing system may obtain a first network event data set associated with a telephone number, where the first network event data set includes identifier matching data associated with the telephone number. The processing system may next apply an input data set comprising at least the first network event data set to a machine learning model implemented by the processing system to obtain an authenticity score associated with the telephone number, where the machine learning model is configured to generate the authenticity score associated with the telephone number in accordance with the input data set. The processing system may next obtain a request from a first authentication service for the authenticity score associated with the telephone number and may transmit the authenticity score associated with the telephone number to the first authentication service in response to the request.