Vulnerability Check Generation via Neural Network and Inductive Logic Programming
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Solution Overview
Problem
Existing security management systems face challenges in efficiently processing and maintaining vulnerability data from sources like HTML and CSS, leading to high maintenance costs and time consumption, which can divert resources from reducing technical debt or adding features.
Innovation Solution
A machine learning-based approach using natural language processing (NLP), inductive logic programming (ILP), and automation to process web content, generate vulnerability checks, and determine exploitability, replacing traditional parsers and allowing for easier data source integration and increased efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional parsers based on HTML and CSS tags are used to process vulnerability data, then the system can extract structured information from vendor advisories and NVD, but the system requires significant maintenance effort and time consumption
Solution Approach 1:
The patent replaces traditional mechanical parsing systems (HTML/CSS tag-based parsers) with a machine learning-based natural language processing system. This substitution eliminates the need for manual parser maintenance while maintaining accurate extraction of vulnerability information from unstructured sources like vendor advisories and security bulletins.
Solution Approach 2:
The machine learning model automatically adapts to changes in data source formats and structures without requiring manual intervention. The system self-trains on new data patterns, automatically updating its extraction capabilities when faced with volatility in HTML structures or CSS tag changes, thereby eliminating the need for developer maintenance time.
2Productivity
If developers spend time maintaining parsers for vulnerability data extraction, then the system can continue to process vulnerability information, but this time could be better spent on reducing technical debt or adding features
Solution Approach 1:
By replacing manual parser maintenance with an automated machine learning system, developers are freed from repetitive maintenance tasks. The ML model handles all data extraction and processing automatically, allowing developers to reallocate their time to higher-value activities such as feature development and technical debt reduction while maintaining or improving vulnerability assessment throughput.
Solution Approach 2:
The machine learning model acts as an intermediary between raw vulnerability data sources and the security analysis system. It handles all the complex work of data extraction, cleaning, and structuring, serving as an autonomous mediator that eliminates the need for developer intervention in the data processing pipeline while maintaining high productivity.
3Adaptability or versatility
If the system processes vulnerability data from multiple volatile sources, then comprehensive vulnerability coverage is achieved, but the volatility of data sources increases parsing difficulty and maintenance requirements
Solution Approach 1:
The patent replaces complex mechanical parsing systems with a machine learning-based natural language processing approach. This substitution enables the system to handle volatility in multiple data sources uniformly, as the ML model learns patterns from diverse sources and adapts to their changing structures without requiring separate parsers for each source, thereby reducing overall system complexity.
Solution Approach 2:
The machine learning model serves as a universal processor for all vulnerability data sources, regardless of their format or volatility. Instead of maintaining separate parsers for different HTML structures and CSS tags from various sources, a single ML-based system handles all data extraction tasks, simplifying the system architecture while maintaining comprehensive data source coverage.
Data Source
AI summary
Various embodiments include systems and methods to implement a process for generating vulnerability check information for performing vulnerability assessments associated with security vulnerabilities. Vulnerability information corresponding to a security vulnerability is input into a multi-headed neural network. An extractive summary of the vulnerability information is output via a vulnerability check head of the multi-headed neural network. Synthetic scan results forming a set of positive examples are generated based at least in part on the extractive summary. An inductive logic programming system is implemented that uses the positive examples and predefined negative examples as inputs to determine, using a set covering algorithm, a general logic program that matches the positive examples and does not match the negative examples. Based at least in part on the general logic program, vulnerability check information is generated for performing a vulnerability assessment associated with the security vulnerability.