Email Spam Detection via Word Length Sequence Analysis
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Solution Overview
Problem
Conventional spam detection methods, relying on sender reputation and message analysis, become ineffective due to sophisticated spamming techniques involving botnets and Network Address Translation, which reduce the accuracy of IP reputation checks and keyword-based filtering.
Innovation Solution
A computer-implemented spam detection method that converts email messages into sequences of word lengths, compares them to a spam table using edit distances like Levenshtein Distance, and updates the table based on user feedback and remote detections, effectively flagging spam messages and adapting to new patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional spam detection methods use sender reputation and message analysis, then spam detection can be performed, but detection accuracy deteriorates due to sophisticated spamming techniques like botnets and NAT
Solution Approach 1:
The patent segments the email message into individual words and represents each word by its length, creating a sequence of word lengths. This segmentation approach transforms the message into a format that can be compared using edit distance, enabling detection of spam variants without relying on sender reputation or keyword matching that are easily circumvented by sophisticated spammers
Solution Approach 2:
The patent changes the parameter representation from actual message content to word length sequences. By comparing edit distances between word length sequences rather than raw text, the system maintains detection accuracy against spam variants while being insensitive to minor textual modifications that spammers introduce
2Measurement precision
If spam detection uses exact matching or keyword filtering, then simple spam can be detected, but detection accuracy deteriorates when spammers make minor edits to messages
Solution Approach 1:
The patent introduces word length sequences as an intermediary representation between the original message and the comparison process. This intermediary format allows the system to detect semantic similarity while being invariant to minor textual edits, preserving detection precision against both exact matches and variants
Solution Approach 2:
The patent creates a composite representation by combining word length information into a sequence that captures the structural pattern of the message. This composite structure enables robust comparison that tolerates minor variations while maintaining sensitivity to spam patterns
Data Source
AI summary
The present disclosure provides systems and methods for detecting email spam and variants thereof. The systems and methods are configured to detect spam messages and variations thereof for different senders and with slight differences within the message body. In an exemplary embodiment, an incoming message body (m) is converted to a sequence of successive word lengths (Sm): m->Sm, a comparison is performed between the sequence, Sm, and a plurality of stored sequences (Sk) of known spam messages, and the incoming message is flagged as spam based on the comparison. Further, the plurality of stored sequences, Sk, may be continually updated based on user feedback and other spam detection techniques. The systems and methods of the present invention may be implemented through a computer, such as a mail server, through a cloud-based security system, through a user's computer via a software agent, and the like.