Neural Network Vector Embeddings for Software Vulnerability Detection
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Solution Overview
Problem
Existing technologies lack an effective method to detect and monitor changes in vulnerabilities within software systems over time, which are crucial for identifying increased vulnerabilities and triggering appropriate protective measures.
Innovation Solution
A computer-implemented method using a neural network to generate vector representations of software systems based on known vulnerabilities, allowing for the comparison of these representations over time to detect changes indicative of increased vulnerabilities, and triggering protective or remediation measures accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vulnerability detection methods are used, then detection capability is limited, but system complexity and resource consumption increase
Solution Approach 1:
The patent replaces traditional mechanical vulnerability scanning and analysis systems with a neural network-based intelligent system. The neural network automatically learns vulnerability patterns from software code and documentation, eliminating the need for complex rule-based detection systems and manual analysis processes.
Solution Approach 2:
The patent creates vector representations (embeddings) that are simplified copies or approximations of complex software vulnerability characteristics. These vector representations capture essential vulnerability features in a compressed form that can be efficiently processed and compared, reducing computational complexity while maintaining detection accuracy.
2Measurement precision
If comprehensive vulnerability analysis is performed, then detection accuracy improves, but analysis time increases
Solution Approach 1:
The patent performs preliminary processing by converting software code and documentation into vector representations in advance. These pre-computed embeddings capture vulnerability-relevant features without requiring full analysis at detection time, enabling rapid comparison and identification of vulnerable components when needed.
Solution Approach 2:
The patent transforms complex software vulnerability data into simplified vector parameter representations. By changing the data format from raw code and documentation to compact vector embeddings, the system maintains comprehensive analysis capability while dramatically reducing the time required for vulnerability detection and comparison operations.
3Reliability
If monitoring of software vulnerabilities over time is implemented, then vulnerability change detection is enabled, but computational resources increase
Solution Approach 1:
The patent creates compact vector representations that serve as efficient copies of software vulnerability states at different time points. These embeddings can be stored and compared with minimal computational overhead, enabling continuous monitoring of vulnerability changes over time without consuming excessive computational resources.
Solution Approach 2:
The patent transforms vulnerability data into simplified vector parameters that maintain the essential information needed for change detection while requiring far less computational power to process and compare across multiple time periods, enabling sustainable long-term monitoring.
Data Source
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AI summary
A computer implemented method of detecting an increased vulnerability of a software system including a plurality of software components, the method comprising: generating a vector representation of each software component derived from a neural network trained using training data defined from known vulnerabilities of the software components in the software system, wherein the training data is augmented by replicating each of one or more training data items in the training data responsive to one or more attributes of a vulnerability corresponding to the training data item; aggregating the vector representations for the software component to an aggregate vector representation for a particular time; repeating the generating and aggregating steps for a plurality of points in time to generate multiple generations of aggregate vector representations; comparing the multiple generations of aggregate vector representations to detect a change in an aggregate vector representation exceeding a maximum threshold degree of change as an indication of an increased vulnerability of the software system.