Spam Flood Detection via HTML Signatures and Posting Frequency
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Solution Overview
Problem
Current spam filtering technologies are inadequate in detecting and managing spam floods, particularly those involving high volumes of spam web pages and blogs with embedded spam content using fixed HTML templates, which can evade detection by generating numerous posts quickly.
Innovation Solution
A computer-implemented methodology that analyzes overall features of blogs and websites, including post volume and frequency, and HTML signatures to identify spam, using a subset of HTML tags to generate a signature and flag spam content based on occurrence thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spam filtering analyzes individual content items, then detection precision improves, but processing time increases significantly when faced with high-volume spam floods
Solution Approach 1:
The patent segments spam detection into two levels: (1) global blog/website level analysis using HTML signatures and posting patterns, and (2) individual content level analysis. This segmentation allows the system to efficiently filter entire blogs/websites as spam sources without analyzing each individual post, thereby reducing processing time while maintaining detection precision.
Solution Approach 2:
The system performs preliminary analysis by generating HTML signatures from a subset of HTML tags and establishing posting pattern baselines before spam floods occur. These pre-computed signatures and patterns enable rapid identification of spam sources during high-volume spam floods without requiring real-time analysis of each content item.
2Measurement precision
If spam filtering analyzes all HTML content, then detection precision improves, but device complexity and processing overhead increase
Solution Approach 1:
The patent applies local quality by selecting only a subset of HTML tags (such as title, h1-h6, p, a, img, etc.) that are most indicative of spam content, rather than analyzing the entire HTML document. This selective approach maintains detection precision for spam identification while significantly reducing processing complexity and resource requirements.
3Measurement precision
If the system monitors all blog posts individually, then spam detection accuracy improves, but productivity decreases due to high processing volume
Solution Approach 1:
The patent merges multiple individual blog/website analyses into a unified spam flood detection system that identifies coordinated spam campaigns. By combining HTML signature matching with posting pattern analysis across multiple blogs, the system achieves high detection accuracy for spam floods while processing them as a single coordinated event rather than individual posts, thereby improving throughput.
4Reliability
If the system uses comprehensive HTML analysis, then detection reliability improves, but processing speed decreases during spam floods
Solution Approach 1:
The system performs preliminary generation of HTML signatures from selected tags and establishes posting pattern baselines before spam floods occur. These pre-computed elements enable rapid matching and identification during spam floods, maintaining high detection reliability while achieving fast processing speeds through efficient pattern matching rather than comprehensive real-time analysis.
Data Source
AI summary
A computer-implemented method and system are provided in which characteristics of a website are analyzed to determine whether the website represents a potential source of spam, and, in response to the analyzing, flags content of the website as spam content. To analyze the website, a total number of posts associated with the website is computed and a publication frequency for the total number of posts can then be calculated. Based on the computed total number and the calculated publication frequency, it can be determined whether the website in its entirety represents spam content. For instance, the calculated publication frequency can be compared to a threshold frequency, and when the calculated publication frequency is greater than the threshold frequency, the website in its entirety as can be identified and flagged as spam content.