Real-Time A2P Message Analysis for Adaptive Illegitimacy Filtering
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Solution Overview
Problem
Existing systems struggle to identify and filter illegitimate Application to Person (A2P) messaging in real-time, which can abuse network resources and compromise recipient experiences, as senders evolve rapidly and adapt their messaging strategies.
Innovation Solution
A network monitor intercepts A2P messages and employs a machine learning module with multiple models (behavioral, sentiment, link, and geographical) to analyze characteristics, generating combined scores for real-time illegitimacy determination, continuously updating and retraining models to adapt to evolving threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time message analysis is performed using machine learning models, then message filtering accuracy is improved, but system complexity increases
Solution Approach 1:
The machine learning module is divided into multiple specialized models (behavioral model, content model, link model, attachment model, geographical model) that each handle specific aspects of message analysis. This segmentation allows the system to achieve high filtering accuracy through specialized analysis while managing complexity by dividing the overall system into independent, manageable components that can be trained and updated separately.
2Adaptability or versatility
If continuous model retraining is implemented, then adaptability to evolving threats is improved, but computational resources consumed increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing message data and characteristics in a database, preparing training data in advance. This allows the machine learning models to be retrained with new data without requiring real-time computational resources during the actual model update process, thereby maintaining adaptability while reducing immediate computational consumption.
Solution Approach 2:
The system maintains continuous operation by continuously collecting message data, continuously updating the database, and periodically retraining models. This continuous cycle ensures the system remains adapted to evolving threats while distributing the computational burden over time rather than concentrating it in single large processing events.
3Measurement precision
If multiple analysis models are deployed, then detection precision is improved, but processing time increases
Solution Approach 1:
The analysis process is segmented into parallel independent model executions (behavioral, content, link, attachment, geographical models) that can run simultaneously. This segmentation enables the system to achieve high detection precision through comprehensive multi-aspect analysis while minimizing processing time by eliminating sequential dependencies between model evaluations.
Data Source
AI summary
A method for flagging a message may include receiving the message. The method may include determining, utilizing at least one machine learning model, a score for the message based at least in part on one or more characteristics of the message. The score may represent a likelihood that the message is an illegitimate message. The method may include updating a database to include the message, the score, and/or the one or more characteristics associated with the message. The method may include providing at least a portion of data on the database to the at least one machine learning model such that the at least one machine learning model is retrained using the message, the score, and or the one or more characteristics. The method may include determining that the score is greater than a predetermined threshold. The method may include flagging the message as an illegitimate message.