Web Vulnerability Document Segmentation Using NLP Entity Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vulnerability assessment systems face challenges in efficiently processing web content due to the volatility of data sources and the need for significant maintenance of HTML and CSS parsers, consuming developer time and resources.
Innovation Solution
A machine learning-based approach utilizing natural language processing (NLP), inductive logic programming (ILP), and automation to process web content, including a fine-tuned transformer model with Attention with Linear Biases (AliBi) for long input sequences, and a combination of question answering (QA) and probability of exploitation (PoE) tasks to generate synthetic scan results and vulnerability checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If HTML and CSS parsers are used to process vulnerability content, then parsing capability is provided, but significant maintenance is required due to volatility in data sources
Solution Approach 1:
The patent replaces traditional mechanical HTML/CSS parsers with a machine learning-based approach using a transformer model. This substitution eliminates the need for maintaining complex parsers while providing robust processing capability for vulnerable content, directly resolving the contradiction between parsing capability and maintenance time.
Solution Approach 2:
The patent changes the fundamental parameter of content processing from rule-based parsing to model-based understanding. By training a transformer model on vulnerability data, the system achieves adaptability to volatile data sources without requiring maintenance of parsing rules, thus resolving the time loss issue.
2Productivity
If traditional parsers are used to process web content, then processing capability is provided, but developer time is consumed for maintenance
Solution Approach 1:
The patent replaces manual parser development and maintenance with an automated machine learning system. The transformer model processes web content automatically without requiring developer intervention for maintenance, thereby preserving processing capability while eliminating developer time loss.
Solution Approach 2:
The machine learning model provides self-service processing capability, automatically adapting to new web content structures without requiring developer updates. This self-service approach maintains high productivity while freeing developers from maintenance tasks.
3Reliability
If vulnerability content is processed manually, then accuracy can be maintained, but efficiency is reduced
Solution Approach 1:
The patent substitutes manual processing with an automated transformer model that achieves both high accuracy and efficiency. The model processes vulnerability content automatically while maintaining reliable accuracy through its training on comprehensive vulnerability data, thus resolving the efficiency-accuracy contradiction.
Solution Approach 2:
The system incorporates feedback mechanisms where the transformer model continuously learns from processed vulnerability data, improving its accuracy over time while maintaining high processing efficiency. This feedback loop enables the system to achieve both reliability and productivity.
Data Source
AI summary
Various embodiments include systems and methods to implement processing of web content for vulnerability assessments. A plurality of documents comprising web content may be obtained from multiple different web sources, and the documents may be parsed to determine a set of discrete document chunks. Parsing the documents includes determining whether a document satisfies a segmentation condition for segmenting the document into multiple discrete document chunks using a named-entity recognition system configured to segment the document based at least in part on a vulnerability identification. The discrete document chunks may be stored in a database, where vulnerability information is indexed such that each respective entry in the database corresponds to a respective vulnerability identification and a respective discrete document chunk.