ML-driven SBOM generation for component-level vulnerability detection
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Solution Overview
Problem
Current software bill of materials (SBOM) do not provide sufficient granularity to identify specific libraries used by applications, leading to inadequate visibility into licensing and exposure scenarios, and existing security scanning tools often fail to identify software vulnerabilities in these components.
Innovation Solution
A system that uses a machine learning subsystem to onboard applications, determine components and scanning parameters, generate scanning instructions, and build an SBOM, which is then used to initiate a vulnerability scanning engine to identify and remediate security vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SBOM generation methods are used, then the SBOM can be created quickly, but the granularity is insufficient to identify specific libraries and components
Solution Approach 1:
The system segments the SBOM generation process into multiple stages: initial SBOM creation, machine learning-based component analysis, and vulnerability scanning. This segmentation allows the system to achieve fine-grained component detection by breaking down the complex task of identifying specific libraries into manageable steps, thereby improving measurement precision without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary actions by generating an initial SBOM and using machine learning models to pre-identify components and their scanning parameters before the actual vulnerability scanning occurs. This preliminary component detection and parameter determination enables the subsequent scanning process to focus on specific libraries with appropriate scanning configurations, achieving high granularity efficiently.
2Measurement precision
If component scanning parameters are determined for each component, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system dynamically changes scanning parameters based on the specific characteristics of each component identified by the machine learning model. Instead of using fixed scanning parameters for all components, the system adapts parameters such as scan depth, scan type, and target libraries according to each component's properties. This parameter adaptation achieves high detection accuracy while optimizing processing time by applying appropriate scanning intensity to each component.
3Loss of information
If machine learning models are used to identify components, then component detection capability is enhanced, but system resource consumption increases
Solution Approach 1:
The system introduces machine learning models as intermediaries between the initial SBOM and the vulnerability scanning process. These models analyze the application's source code, dependencies, and configuration files to extract component information that would be difficult to obtain through traditional scanning alone. By using ML models as intermediaries to pre-process and enrich component information, the system achieves complete component information without requiring exhaustive resource-consuming scanning of every component.
4Reliability
If vulnerability scanning is performed on all components, then security coverage is maximized, but false positives and scanning overhead increase
Solution Approach 1:
The system applies local quality by determining specific scanning parameters for each component based on its characteristics. Instead of uniformly scanning all components with the same parameters, the system tailors the scanning approach to each component's type, version, and risk profile. This localized scanning strategy maintains comprehensive security coverage while reducing false positives by applying appropriate scanning intensity and methods to each component context.
Data Source
AI summary
Systems, computer program products, and methods are described herein for component-level detection in third party software applications. An example system may receive, from a client device, data packets with payload containing a request to onboard a first application into a computing environment. In response, the system may determine, using an application ingestion subsystem, information associated with the first application, and subsequently deploy, using a machine learning subsystem, a trained machine learning model on the information to various components associated with the first application and component scanning parameters for each of the one or more components. Based on components, the system may build a software bill of materials (SBOM) for the first application.