Text Message Fraud Detection Using Linguistic Error Analysis
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Solution Overview
Problem
Conventional spam filters often fail to accurately distinguish between legitimate and fraudulent messages, leading to excessive filtering or inadequate filtering, with many spam messages going undetected or mistakenly filtering legitimate messages.
Innovation Solution
An apparatus and method that analyze messages for textual patterns typical of non-native language speakers, using rule sets of grammatical and usage errors to improve filtering by combining this analysis with conventional fraud detection techniques, such as keyword recognition and content analysis, to determine the likelihood of a message being fraudulent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spam filters are used, then filtering speed is maintained, but filtering accuracy deteriorates
Solution Approach 1:
The patent combines multiple filtering approaches: conventional keyword-based filtering and a new linguistic analysis component that detects non-native language speaker patterns. This merging allows the system to maintain the speed of conventional filters while adding the accuracy boost from linguistic analysis, resolving the contradiction between filtering speed and accuracy.
Solution Approach 2:
The patent introduces an intermediary linguistic analysis layer between the incoming message and the final filtering decision. This intermediary component analyzes grammatical patterns, sentence structure, and vocabulary usage to identify non-native language speaker characteristics, providing additional accuracy without requiring complete redesign of the filtering system.
2Reliability
If filtering sensitivity is increased to catch more spam, then fraudulent message detection improves, but legitimate message false positives increase
Solution Approach 1:
The patent applies local quality by analyzing specific linguistic characteristics of messages rather than using blanket filtering. It identifies local patterns such as grammatical errors, sentence structure anomalies, and vocabulary usage that are specific to non-native language speakers. This targeted approach allows high sensitivity for spam detection while minimizing false positives by considering the specific linguistic context of each message.
Solution Approach 2:
The patent changes the filtering parameters from simple keyword matching to linguistic parameter analysis. By measuring grammatical correctness, sentence structure complexity, and vocabulary diversity, the system can adjust sensitivity dynamically based on these linguistic parameters, achieving reliable spam detection without excessive false positives.
Data Source
AI summary
An apparatus, and an associated method, detects spam and other fraudulent messages sent to a recipient station. The textual portion of a received message is analyzed to determine whether the message includes errors made by non-native language speakers when authoring a text message. A text analysis engine analyzes the text using rules sets that identify grammatical errors made by non-native language speakers, usage errors made by non-native language speakers, and other errors.