Bulk Messaging Detection Using Homoglyph and URL Analysis
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Solution Overview
Problem
Organizations and individuals face challenges in effectively blocking unwanted and commercial bulk messages, as senders often evade filtering systems by disguising messages as person-to-person communications to avoid higher charges, necessitating advanced automated detection and enforcement methods.
Innovation Solution
A computing platform that tokenizes messages, detects homoglyphs and URLs, and uses machine learning to identify and flag spam or commercial senders, applying enforcement policies to restrict or block messages sent through improper channels, ensuring compliance with application-to-person channels for commercial messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional filtering and blocking systems are used to prevent unwanted messages, then some spam messages can be blocked, but senders can easily evade these systems by changing tactics and disguising messages as P2P communications
Solution Approach 1:
The system dynamically adapts to changing spam tactics by continuously analyzing message patterns, token frequencies, and sender behaviors. The machine learning model is retrained with new data to detect evolving evasion techniques, making the filtering system responsive to adaptive threats rather than static rules
Solution Approach 2:
The system changes detection parameters by analyzing multiple features including token frequency distributions, homoglyph patterns, URL characteristics, and message metadata. By monitoring changes in these parameters over time, the system detects when senders attempt to evade filtering by modifying their messaging patterns
2Loss of energy
If commercial senders disguise bulk messages as P2P messages to avoid higher A2P charges, then they can reduce messaging costs, but this creates unwanted commercial messages in P2P channels
Solution Approach 1:
The system performs preliminary analysis of messages before delivery by tokenizing content, detecting homoglyphs and URLs, and evaluating against trained models to identify commercial intent. This preliminary detection prevents commercial messages from being delivered through P2P channels in the first place
Solution Approach 2:
The system introduces an intermediary detection layer between message submission and delivery that analyzes message characteristics and sender behavior patterns. This intermediary evaluation determines whether a message should be routed through P2P or A2P channels based on detected commercial intent rather than relying on sender declarations
3Measurement precision
If automated detection systems analyze message content using tokenization and pattern recognition, then spam and commercial messages can be identified accurately, but the system complexity increases
Solution Approach 1:
The detection system segments message analysis into distinct modular components: tokenization of message content, detection of homoglyphs, URL analysis, metadata evaluation, and machine learning classification. Each component handles a specific aspect of analysis, making the overall complex system manageable through functional segmentation
Solution Approach 2:
The system introduces tokenization as an intermediary processing step that converts message content into standardized tokens before analysis. This intermediary representation simplifies subsequent pattern matching and machine learning operations by normalizing diverse message formats into comparable token sequences
Data Source
AI summary
Aspects of the disclosure relate to providing commercial and/or spam messaging detection and enforcement. A computing platform may receive a plurality of text messages from a sender. It may then tokenize the plurality of text messages to yield a plurality of tokens. The computing platform may then match one or more tokens of the plurality of tokens in the plurality of text messages to one or more bulk string tokens. Next, it may detect one or more homoglyphs in the plurality of text messages, and then detect one or more URLs in the plurality of text messages. The computing platform may flag the sender based at least on the one or more matching tokens, the one or more detected homoglyphs, and the one or more detected URLs. Based on flagging the sender, the computing platform may block one or more messages from the sender.