Regex Engine for Anti-Spam Signatures via Token Graph Analysis
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Solution Overview
Problem
Existing regex signature generation techniques are manually intensive, prone to errors, and slow, requiring significant time and resources to generate signatures from large volumes of messages, with tools being limited in capacity and efficiency.
Innovation Solution
The development of a regex engine that automatically generates anti-spam signatures by creating a token graph from ordered sub-strings of messages, identifying pivots, and converting them into regular expressions, allowing for the rapid analysis of thousands of strings to produce accurate signatures within seconds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual regex signature generation techniques are used, then accuracy can be maintained through human expertise, but productivity is severely limited and the process becomes manually intensive
Solution Approach 1:
The system performs self-service by automatically analyzing spam message clusters, generating token graphs, identifying pivots, and producing regex signatures without human intervention. The regex engine autonomously completes the entire signature generation workflow that previously required manual researcher effort.
Solution Approach 2:
The patent replaces the mechanical manual process of regex creation with an automated computational system. The regex engine uses algorithmic processing to substitute human researchers' manual pattern recognition and signature creation work with automated token graph analysis and pivot identification.
2Productivity
If tools are designed to handle large volumes of messages, then productivity increases, but device complexity and computational resources required increase significantly
Solution Approach 1:
The system segments the message analysis process into distinct computational stages: clustering messages by similarity, generating token graphs from substrings, identifying pivots within clusters, and converting pivots to regex signatures. This segmentation allows each stage to be optimized independently and processed efficiently.
Solution Approach 2:
The system performs preliminary actions by pre-processing messages into clusters based on similarity before generating signatures. Token graphs are constructed in advance from message substrings, and pivots are pre-identified within clusters, enabling rapid signature generation when needed.
3Adaptability or versatility
If existing tools are used to generate signatures, then some automation is achieved, but the tools are slow and limited in the number of strings they can analyze
Solution Approach 1:
The system merges multiple functionally similar operations into unified processes. The token graph generation combines substring extraction, clustering, and pattern recognition into a single efficient operation. The pivot identification merges frequency analysis, position analysis, and significance determination into one consolidated step.
Solution Approach 2:
The system changes key parameters of the analysis process by adjusting cluster size thresholds, token length ranges, and pivot frequency cutoffs to optimize for both speed and comprehensiveness. These parameter adjustments enable the system to analyze larger numbers of strings faster while maintaining signature quality.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture to generate regex and detect data similarity are disclosed. An example apparatus includes a token graph generator to generate a token graph including nodes based on a cluster of strings corresponding to a group of messages that are known to be spam; a pivot engine to identify pivot nodes in the cluster of strings; a pivot applicator to tag corresponding ones of the nodes of the token graph as the pivot nodes; and a regex converter to generate the anti-spam signature based on: (a) the tagged nodes and (b) at least one of the node of the token graph that is not tagged as a pivot node.