Auxiliary Spam Detection Model Segmentation for Obfuscation Handling
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Solution Overview
Problem
Current spam filtering technologies face challenges in efficiently detecting obfuscated words, leading to a tradeoff between accuracy and computational performance, which is impractical for large-scale spam classification due to high computational costs and slow de-obfuscation rates.
Innovation Solution
A computer-implemented system integrates auxiliary spam detection models with a base spam detection model to improve accuracy and efficiency by employing an obfuscation detection model that classifies words as 'obfuscated' or 'true' and provides these features to the spam classifier, using machine learning algorithms like logistic regression for weight generation and feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If de-obfuscation techniques are used to improve spam detection accuracy, then detection accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the spam detection process into two independent stages: first, a lightweight obfuscation detection model identifies potential obfuscated words using simple feature extraction (character n-grams, word length, position); second, these detected obfuscated words are used as features in the main spam classification model. This segmentation avoids the computational burden of full de-obfuscation while maintaining high detection accuracy.
Solution Approach 2:
The patent applies preliminary action by using the obfuscation detection model to pre-identify and flag obfuscated words before the main spam classification process. This preliminary detection creates a curated set of suspicious words that are then used as features, eliminating the need for computationally expensive real-time de-obfuscation during the main classification stage.
2Measurement precision
If de-obfuscation techniques are used to improve spam detection accuracy, then detection accuracy is improved, but email delivery time increases
Solution Approach 1:
The patent segments the processing pipeline so that obfuscation detection uses a lightweight, fast model that operates independently and in parallel with main message processing. The simple feature extraction (character patterns, word statistics) can be executed rapidly without blocking email delivery, unlike comprehensive de-obfuscation techniques.
Solution Approach 2:
The patent employs a simple, disposable obfuscation detection model that sacrifices comprehensive de-obfuscation capability for speed. Instead of investing heavy computational resources in accurate de-obfuscation, the system uses a fast, approximate detection approach that identifies suspicious patterns without attempting to recover original words, enabling rapid processing.
3Measurement precision
If auxiliary classification models are integrated to improve feature extraction, then spam detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges the obfuscation detection model with the main spam classification system in a unified architecture. The obfuscation detection component is integrated as a feature extraction stage that feeds directly into the spam classifier, sharing the same processing pipeline and data structures. This merging reduces overall system complexity compared to having separate, independent systems.
Solution Approach 2:
The obfuscation detection model serves multiple functions: it identifies obfuscated words, extracts feature patterns from them, and provides inputs to the spam classification model. This multi-functionality eliminates the need for separate specialized components for each task, reducing system complexity while maintaining comprehensive spam detection capability.
Data Source
AI summary
A computer-implemented system and method are described for integrating a series of auxiliary spam detection models with a base spam detection model, thereby improving the accuracy and efficiency of the overall spam detection engine. For example, a system according to one embodiment of the invention comprises: a base spam filter feature extractor to detect a first set of features from incoming email messages; one or more auxiliary model feature extractors, each of the auxiliary model feature extractors to detect a different set of features from the incoming email messages; one or more auxiliary detection modules, each of the auxiliary detection modules to receive an indication of the different sets of features detected by a corresponding one of the auxiliary model feature extractor modules and to apply weights to the detected features; and a base spam filter module to receive an indication of the first set of features from the base spam filter feature extractor and the weights generated by the auxiliary detection modules, the base spam filter module to assign base spam filter weights to the first set of features and to determine whether an email message is spam based on the weights of the first set of features and the weights of the different set of features identified by the auxiliary model feature extractors.