Telecommunication Network Verification for SIM Hijacking Defense
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Solution Overview
Problem
Existing two-factor authentication systems relying on mobile devices are vulnerable to hijacking by attackers who gain possession of the device or its subscriber identity module (SIM), compromising the security of online accounts with sensitive information.
Innovation Solution
A machine learning model within a telecommunication network generates an authenticity score based on network event data associated with a telephone number, using indicators such as identifier matching, anomalous behavior, device location, malware presence, and user-specified preferences to determine the legitimacy of the device possession.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If two-factor authentication using mobile devices is implemented, then account security is improved, but the system becomes vulnerable to SIM hijacking and device compromise
Solution Approach 1:
The patent introduces a telecommunication network intermediary that sits between the authentication service and the mobile device. This intermediary monitors network events, analyzes device behavior patterns, and provides authenticity verification without requiring direct interaction with the user's mobile device, thereby adding a security layer that is independent of device possession
Solution Approach 2:
The system implements continuous feedback loops by monitoring network events related to the telephone number and device identifiers. The machine learning model processes this feedback data to dynamically assess authenticity scores, allowing the system to adapt to changing threat patterns and detect anomalies in real-time
2Measurement precision
If machine learning analysis of network events is implemented, then detection accuracy of hijacked devices is improved, but system complexity increases
Solution Approach 1:
The telecommunication network performs multiple functions simultaneously: it routes communications, monitors network events, collects data for analysis, and provides authenticity verification. By leveraging existing network infrastructure for these diverse purposes, the system avoids the need for separate dedicated systems for each function, thereby managing complexity while maintaining high detection accuracy
3Reliability
If additional authentication verification layers are added, then security against unauthorized access is improved, but authentication process time increases
Solution Approach 1:
The system performs preliminary analysis of network events and device behavior patterns continuously in the background before authentication is actually needed. The machine learning model pre-processes and stores authenticity indicators, so that when authentication is required, the verification can be completed rapidly by retrieving pre-analyzed data rather than conducting full analysis in real-time
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.