Vulnerability Detection Using Hierarchical Attention Network
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Solution Overview
Problem
Existing technologies face challenges in efficiently identifying and mitigating vulnerabilities in open-source software, leading to potential security risks due to the high cost and limited reach of human resources dedicated to vulnerability tracking.
Innovation Solution
The use of automated methods, specifically text classification using natural language processing (NLP) and machine learning techniques, such as the Hierarchical Attention Network (HAN) model with Virtual Adversarial Training (VAT), to identify vulnerabilities in computer program code by classifying security-related texts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human resources are dedicated to vulnerability tracking, then vulnerability detection capability is improved, but cost and scalability deteriorate
Solution Approach 1:
The patent replaces manual human analysis of vulnerability reports with an automated machine learning system. The model processes text from vulnerability databases and GitHub repositories automatically, substituting the mechanical human effort with an automated computational system that maintains high detection precision while eliminating the scalability and cost limitations of manual tracking.
Solution Approach 2:
The system creates a computational model that learns from labeled vulnerability data and replicates expert human judgment capabilities. By training on historical vulnerability reports and their resolutions, the model copies the detection patterns and knowledge of security experts, enabling automated analysis at scale without requiring additional human expertise.
2Measurement precision
If more data is collected for training, then model accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the training data into two distinct sources: structured vulnerability database entries and unstructured GitHub discussion posts. This segmentation allows the model to process different data types through appropriate pathways while maintaining overall system coherence. The segmented approach enables efficient handling of large datasets by treating each source according to its specific characteristics rather than attempting uniform processing.
Solution Approach 2:
The system transitions from processing only structured vulnerability data to incorporating unstructured text data from GitHub discussions as an additional dimension. This dimensional expansion enriches the training dataset with contextual information about vulnerability discussions, resolutions, and community responses, improving model accuracy while managing complexity through appropriate text processing techniques.
3Productivity
If automated text classification is implemented, then vulnerability identification efficiency is improved, but classification precision may deteriorate
Solution Approach 1:
The patent applies preliminary filtering and preprocessing to vulnerability reports before they reach the main classification model. The system pre-processes text data, extracts relevant features, and prepares inputs in advance, which improves the efficiency of the automated classification process while maintaining precision by ensuring high-quality input data for the classification algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms where the model's predictions are continuously refined based on performance metrics and comparison with actual vulnerability outcomes. This feedback loop allows the automated system to learn from its classifications, improving precision over time while maintaining high identification efficiency through automated processing.
Data Source
AI summary
Open-source software is prevalent in the development of new technologies. Monitoring software updates for vulnerabilities is expensive and time consuming. Online discussions surrounding new software updates can often provide vital information regarding emerging risks. It is presented a novel approach for automating surveillance of software through the use of natural language processing methods on open-source issues. Further, the potential of virtual adversarial training, a popular semi-supervised learning technique, is used to leverage the vast amounts of unlabeled data available to achieve improved performance. On industry data, it is found that a hierarchical attention network with virtual adversarial training that utilizes the innate document structure to encapsulate the text can be used with good results.