Machine Learning Feature Sharing for SIM Swap Risk Detection
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Solution Overview
Problem
Existing multi-factor authentication systems are vulnerable to SIM swap fraud, where attackers hijack a user's SIM card to bypass secondary verification mechanisms, leading to unauthorized access to sensitive accounts.
Innovation Solution
A machine learning model is trained using data from multiple service providers to generate a risk score for SIM swap fraud, enabling remedial actions such as blocking unauthorized SIM swaps and alerting relevant entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-factor authentication is implemented using mobile device input, then account security is improved, but the system becomes vulnerable to SIM swap fraud attacks
Solution Approach 1:
The system performs preliminary risk assessment by analyzing input data sets from multiple service providers before allowing SIM swap operations. A machine learning model evaluates the risk score in advance, and remedial actions are prepared and can be automatically executed if the risk exceeds thresholds, preventing fraudulent SIM swaps before they complete
Solution Approach 2:
The patent introduces an intermediary risk assessment system between the user and the SIM swap process. This intermediary layer collects data from multiple service providers, processes it through a machine learning model, and mediates the SIM swap request by either allowing it to proceed or blocking it based on the calculated risk score, thus protecting the authentication system without completely blocking legitimate users
2Measurement precision
If data from multiple service providers is collected and processed through a machine learning model, then SIM swap fraud detection accuracy is improved, but system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it processes input data sets from various service providers, calculates risk scores for SIM swap operations, and provides the basis for remedial actions. This multi-functional approach consolidates what could be separate systems into a single unified model, reducing overall system complexity while maintaining high detection accuracy
Solution Approach 2:
The system automatically collects data from multiple service providers, processes it through the machine learning model, and executes remedial actions without requiring manual intervention. This self-service capability reduces operational complexity while improving detection precision through consistent automated application of the risk assessment algorithm
Data Source
AI summary
A processing system including at least one processor may obtain a first input data set associated with a telephone number from a first service provider that implements a multi-factor authentication process for permitting an access to a service of the first service provider and may apply at least the first input data set to a machine learning model implemented by the processing system to obtain a risk score associated with the telephone number for a subscriber identity module swap of a subscriber identity module, where the machine learning model is trained to generate the risk score associated with the telephone number in accordance with at least the first input data set. The processing system may then perform at least one remedial action associated with the telephone number and the subscriber identity module, in response to the risk score.