Spam Detection via Incorrect Punctuation Token Signatures
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Solution Overview
Problem
Conventional spam-detection software struggles to accurately identify spam messages with poor spelling, punctuation, and grammar, often producing false positives due to legitimate email errors and non-English text misclassification.
Innovation Solution
The system extracts text signatures from spam messages by identifying incorrectly punctuated tokens and adjacent tokens, creating candidate signatures that include these, and ranks them based on misspelling ratios, whitespace, and capitalization to accurately identify future spam instances without false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional spam-detection software detects misspelled words and punctuation errors, then spam detection capability is improved, but false positives increase due to legitimate email errors and non-English text
Solution Approach 1:
The patent segments the detection process into multiple stages: tokenization of text into individual words and punctuation marks, identification of incorrectly punctuated tokens, selection of adjacent tokens, and creation of candidate signatures. This segmentation allows the system to focus on specific error patterns rather than applying blanket detection rules, thereby improving accuracy while reducing false positives.
Solution Approach 2:
The patent applies local quality by creating context-specific detection rules. Instead of treating all misspelled words equally, the system identifies incorrectly punctuated tokens and selects adjacent tokens to form localized signatures. This allows the detection to be tailored to specific error patterns found in spam messages while preserving legitimate variations in proper English text.
2Measurement precision
If spam-detection software uses strict punctuation and spelling rules, then detection precision is improved, but adaptability to variations in spam messages deteriorates
Solution Approach 1:
The patent implements dynamics by making the detection system adaptive rather than static. The system dynamically creates candidate signatures by identifying incorrectly punctuated tokens and selecting adjacent tokens based on the specific characteristics of each spam message. This dynamic approach allows the system to adapt to various spam message patterns while maintaining precise detection through the structured signature creation process.
Solution Approach 2:
The patent applies parameter changes by modifying the detection approach based on the specific parameters of each message. The system changes parameters such as token selection criteria, signature length, and detection thresholds based on the characteristics of the messages being analyzed. This allows the system to maintain high precision across different spam variations by adjusting detection parameters rather than relying on fixed rules.
3Reliability
If the system creates detailed text signatures with multiple adjacent tokens, then spam identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies the extraction principle by isolating the critical elements needed for detection. Instead of analyzing entire messages or using complex machine learning models, the system extracts specifically incorrectly punctuated tokens and a limited number of adjacent tokens to create compact signatures. This extraction approach maintains high identification accuracy while significantly reducing computational complexity compared to analyzing entire messages or using sophisticated algorithms.
Data Source
AI summary
A computer-implemented method for creating text signatures for identifying spam messages may include: 1) identifying a spam message, 2) tokenizing the text of the spam message, 3) identifying at least one incorrectly punctuated token within the tokenized text of the spam message, 4) identifying, within the tokenized text of the spam message, at least one token that is adjacent to the incorrectly punctuated token, and then 5) creating a candidate signature for identifying future instances and/or variations of the spam message that includes both the incorrectly punctuated token and the token that is adjacent to the incorrectly punctuated token. Corresponding systems and configured computer-readable media are also disclosed.