AI Message Scoring for Gray-Route A2P Filtering
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Solution Overview
Problem
Existing communication networks face challenges in identifying and filtering gray-routed application-to-person (A2P) messages, which are illegitimately routed to avoid detection and proper charging, posing issues in network integrity and user security.
Innovation Solution
A cloud-based system utilizing a machine learning model (MLM) analyzes A2P messages for characteristics such as content, geographical data, and network information to assign a score indicating the likelihood of gray-routing, and filters such messages using a contextual filtering system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional filtering methods are used to detect gray-routed messages, then network infrastructure complexity remains low, but detection accuracy and reliability are insufficient
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the message routing system and the filtering system. The ML model receives message characteristics, processes them through trained patterns, and outputs detection scores that guide the filtering decisions, enabling accurate gray-route detection without directly analyzing every message detail in the traditional filtering path
Solution Approach 2:
Traditional mechanical filtering rules and pattern matching are replaced with a machine learning-based detection system. The ML model uses trained patterns from historical data to automatically identify gray-routed messages, substituting complex manual rule-based systems with an automated intelligent system that adapts to evolving routing techniques
2Measurement precision
If machine learning models analyze all message characteristics in detail, then detection precision improves, but processing time and computational resources increase
Solution Approach 1:
The machine learning model is pre-trained on historical message data and routing patterns before actual detection occurs. This preliminary training allows the model to make accurate detection decisions quickly during operation, as it only needs to evaluate current messages against already-learned patterns rather than analyzing everything from scratch
Solution Approach 2:
Instead of analyzing every possible message characteristic in full detail, the system focuses on the most discriminative features that the ML model has identified as most important for detection. This selective analysis of critical characteristics achieves high precision while reducing processing time compared to exhaustive analysis
3Object-affected harmful factors
If gray-routed messages are filtered aggressively, then network security improves, but legitimate messages may be blocked reducing productivity
Solution Approach 1:
The system continuously monitors detection results and message delivery outcomes to refine the machine learning model's decision boundaries. Feedback from both detected gray-routes and potentially blocked legitimate messages allows the model to adjust its filtering thresholds, improving security while reducing false positives that would harm productivity
Solution Approach 2:
The filtering system dynamically adjusts detection parameters and thresholds based on contextual information about each message and the current network state. By changing parameters such as detection sensitivity and filtering thresholds on a per-message basis, the system can be aggressive against known gray-routes while being more permissive toward legitimate traffic patterns
Data Source
AI summary
A method for detecting gray routed messages may include receiving a message from a sender via a network. The method may include determining one or more characteristics of the message. The method may include providing the one or more characteristics to a machine learning model, the machine learning model configured to assign a score to the message based at least in part on the one or more characteristics of the message. The method may include determining that the message is a gray-routed message based at least in part on the score assigned to the message. Based on a determination that the message is a gray-routed message, the method may include transmitting data indicating the message, the one or more characteristics of the message, and the sender to a contextual filtering system, the contextual filtering system configured to identify and filter gray-routed messages from the network.