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Support Vector Machine-Based Software Component Risk Classification in Mattermost Using SBOM Data and Public Vulnerability Records

Description

Modern software applications depend on third-party libraries, frameworks, and packages to provide application functionality. These dependencies can introduce security risks when they contain known vulnerabilities, use outdated versions, or depend on other vulnerable components.

A Software Bill of Materials (SBOM) provides an inventory of software components used by an application. It can contain information such as component names, versions, package identifiers, and dependency relationships, depending on the SBOM format and generation process.

Although an SBOM helps identify software components, it does not independently establish whether those components contain vulnerabilities. Vulnerability information must be correlated with reliable security records to determine which components require further investigation.

In this use case, Mattermost is used as the existing software application for software supply-chain risk analysis. An SBOM is generated from a supported Mattermost build or its available dependency artifacts within an isolated Ubuntu and Kali Linux laboratory environment.

Public vulnerability records are obtained from an existing source such as OSV.dev. The collected SBOM components are matched against available vulnerability information, and the resulting component records are prepared for machine-learning analysis.

A Support Vector Machine (SVM) classifier is used to classify software components into risk categories based on suitable features and verified training labels. The model is evaluated using held-out data to measure its classification performance.

Python integrates SBOM processing, vulnerability matching, feature extraction, model training, risk classification, and report generation. A Streamlit dashboard presents the component inventory, known vulnerability findings, predicted risk categories, and model evaluation results.

The complete workflow is:
Mattermost Application
→
SBOM Generation
→
Component Extraction
→
Public Vulnerability Data Collection
→
Vulnerability Matching
→
Data Preprocessing
→
Feature Extraction
→
SVM Training
→
Component Risk Classification
→
Security Verification
→
SQLite Storage
→
Streamlit Dashboard
→
Risk-Based Review

Existing Security Problem

Application: Mattermost

Mattermost is an existing collaboration and messaging application that can be deployed in a controlled laboratory environment.

Like other software applications, Mattermost relies on software components and dependencies that may introduce supply-chain security risks.

The application is used as the target for examining dependency information, correlating software components with public vulnerability records, and evaluating the proposed machine-learning-based risk-classification workflow.

Existing Problem :

Software applications can contain many direct and transitive dependencies. Identifying the security status of every component can be difficult when component information and vulnerability records are maintained separately.

A vulnerable dependency may not be immediately apparent from the application's user interface. Security analysts may need to examine the SBOM, identify component versions, compare them with vulnerability records, and determine which components should receive further attention.

Traditional vulnerability scanners help identify known vulnerabilities, but the resulting findings may still require prioritization and contextual analysis.

The security problem is:
Mattermost Application
→
Third-Party Software Dependencies
→
Direct and Transitive Components
→
Potentially Vulnerable Software Versions
→
SBOM Component Inventory
→
Public Vulnerability Record Matching
→
Multiple Component-Level Findings
→
Manual Risk Review and Prioritization
→
Requirement for AI-Assisted Component Risk Classification

The proposed solution combines SBOM analysis, public vulnerability records, and an SVM classifier to organize software components into risk categories.

Attack

Specific Attack: Software Supply-Chain Risk Scenario Involving a Vulnerable Third-Party Dependency

The controlled security-testing scenario examines how a third-party software dependency with a known vulnerability can introduce risk into an application.

Mattermost is deployed within the authorized laboratory environment. Its available dependency artifacts are examined to identify software components and their versions.

Public vulnerability records are used to identify components that match known vulnerable versions, where matching information is available.

For controlled validation, the analysis may also include a compatible, known-vulnerable test component in an isolated test project or SBOM. This test component is used to verify the vulnerability-matching and risk-classification workflow; it is not introduced into a production Mattermost deployment.

The objective is to determine whether the workflow can identify known vulnerability evidence, classify suitable component records, and display the results for security review.

Specific Attack: Software Supply-Chain Risk Scenario Involving a Vulnerable Third-Party Dependency

The controlled security-testing scenario examines how a third-party software dependency with a known vulnerability can introduce risk into an application.

Mattermost is deployed within the authorized laboratory environment. Its available dependency artifacts are examined to identify software components and their versions.

Public vulnerability records are used to identify components that match known vulnerable versions, where matching information is available.

For controlled validation, the analysis may also include a compatible, known-vulnerable test component in an isolated test project or SBOM. This test component is used to verify the vulnerability-matching and risk-classification workflow; it is not introduced into a production Mattermost deployment.

The objective is to determine whether the workflow can identify known vulnerability evidence, classify suitable component records, and display the results for security review.

Attack Behavior :

Third-Party Software Component

↓

Known Vulnerability Associated with a Specific Version\n↓\n Vulnerable Component Included in a Controlled Test Scenario

↓

SBOM Component Identification

↓

Public Vulnerability Record Matching

↓

Component Metadata and Vulnerability Evidence Collection

↓

SVM-Based Risk Classification

↓

Predicted Risk Category Generation

↓

Known Vulnerability Verification

↓

Dashboard Reporting and Security Review

Security Concept

AI-Based SBOM Component Risk Classification and Vulnerability Evidence Correlation

The primary security concept is AI-Based Software Supply-Chain Risk Classification. The workflow combines software component inventory, public vulnerability information, and machine-learning classification to support dependency risk assessment.

SBOM-Based Component Identification : An SBOM is generated from a supported Mattermost build or its available dependency artifacts. Component names, versions, package identifiers, and other available metadata are extracted for analysis.

SVM-Based Risk Classification : A Support Vector Machine classifier is trained using an existing dataset containing suitable component features and verified risk labels. The model evaluates compatible component records and predicts their risk categories. The training data must support the chosen classification objective; known vulnerability matches alone should not automatically be treated as a complete measure of overall risk.

Vulnerability Correlation and Dashboard Reporting : Component records are correlated with public vulnerability information. The known vulnerability findings and SVM predictions are maintained as separate results so that the analyst can distinguish verified vulnerability evidence from model-estimated risk.

The assessment follows:
Mattermost Application
→
SBOM Generation
→
Component Metadata Extraction
→
Public Vulnerability Data Collection
→
Vulnerability Matching
→
Feature Preparation
→
SVM Model Training
→
Component Risk Classification
→
Known Vulnerability Verification
→
Risk Prioritization
→
SQLite Evidence Storage
→
Streamlit Dashboard
→
Analyst Review

Defensive Mechanism

The security concept defines the overall software supply-chain risk assessment strategy.

The following are the specific defensive mechanisms implemented.

SBOM-Based Component Inventory :

The application’s available software components are identified using an SBOM generated from supported artifacts.

Purpose: To establish a structured inventory of software components and their available metadata.

Public Vulnerability Matching

SBOM components are compared with public vulnerability records using supported package identifiers and version information.

Purpose
  • To identify components that match known vulnerability records.

SVM-Based Risk Classification

The trained SVM model classifies compatible component records using the features and labels supported by the training dataset.

Purpose
  • To estimate component risk categories and support security-review prioritization.

Consistent Feature Processing

Training and inference records are processed using compatible feature-extraction and transformation procedures.

Purpose
  • To ensure that component records are represented consistently during model training and prediction.

Vulnerability Evidence Verification

Known vulnerability matches are reviewed using the available package information and vulnerability records.

Purpose
  • To distinguish verified vulnerability evidence from AI-predicted risk.

Risk-Based Component Prioritization

Component findings and model predictions are organized for further security review.

Purpose
  • To help analysts prioritize components that require additional investigation.

Security Evidence Storage

SBOM records, vulnerability matches, model predictions, and evaluation results are stored in a structured database.

Purpose
  • To maintain traceable evidence for reporting and subsequent analysis.

Dashboard-Based Security Review

The dashboard presents component information, vulnerability findings, and model predictions.

Purpose
  • To support practical review of software supply-chain risk assessment results.

Model Performance Evaluation

The SVM classifier is evaluated against held-out records with suitable reference labels.

Purpose
  • To measure classification performance and identify false-positive and false-negative predictions.

Security Tools

Primary Target Application: Mattermost

Mattermost provides the existing software application used for dependency and SBOM analysis.

Purpose
  • Provide the target application for software component inventory.
  • Supply supported application artifacts for SBOM generation.
  • Support controlled software supply-chain risk assessment.

SBOM Generation Tool: Syft

Syft generates SBOMs from supported software directories, container images, and other compatible artifacts.

Purpose
  • Identify software components.
  • Extract package names and versions.
  • Generate an SBOM in a supported format, such as CycloneDX or SPDX.

Vulnerability Scanning Tool: OSV-Scanner

OSV-Scanner checks supported dependency information against vulnerability data available through OSV.

Purpose
  • Identify known vulnerability matches.
  • Support dependency-level vulnerability assessment.
  • Provide security findings for comparison with model predictions.

Additional Vulnerability Scanner: Grype

Grype scans supported software artifacts and SBOMs for known vulnerabilities.

Purpose
  • Provide an additional vulnerability-scanning result.
  • Support cross-checking of selected findings.
  • Help validate the vulnerability-matching workflow.

Vulnerability Data Source: OSV.dev

OSV.dev provides public vulnerability records for supported software ecosystems.

Purpose
  • Obtain known vulnerability information.
  • Correlate package identifiers and versions with vulnerability records.
  • Support validation of component-level findings

Programming and Automation Tool: Python

Python provides the primary implementation environment.

Purpose
  • Integrate SBOM processing and vulnerability matching
  • Prepare model input features.
  • Train and evaluate the SVM classifier.
  • Generate component-level risk results.
  • Integrate the dashboard and database.

Machine-Learning Library: Scikit-learn

Scikit-learn provides the Support Vector Machine implementation and evaluation utilities.

Purpose
  • Train the SVM classifier.
  • Generate risk-category predictions.
  • Evaluate model performance.
  • Produce classification metrics and confusion matrices.

Data Processing Tool: Pandas

Pandas processes SBOM component records, vulnerability findings, and model results.

Purpose
  • Load and organize component metadata.
  • Merge compatible records.
  • Prepare labelled training data.
  • Organize classification results for reporting.

Numerical Processing Tool: NumPy

NumPy supports numerical operations required during feature preparation and model processing.

Purpose
  • Support numerical data transformations.
  • Prepare model-compatible arrays.
  • Assist with analysis of numerical features.

Feature Preparation Tool: Scikit-learn Preprocessing

Scikit-learn preprocessing utilities prepare suitable categorical and numerical features for the SVM classifier.

Purpose
  • Encode compatible categorical fields.
  • Scale numerical features where appropriate.
  • Maintain consistent transformations between training and inference.

Database: SQLite

SQLite stores the component inventory and security analysis results

Purpose
  • Store SBOM component records.
  • Maintain vulnerability matching results.
  • Record SVM predictions and evaluation findings.
  • Support dashboard queries and report generation.

Dashboard Framework: Streamlit

Streamlit provides the user interface for the risk-analysis workflow.

Purpose
  • Display component inventory.
  • Present known vulnerability findings.
  • Show SVM-predicted risk categories.
  • Visualize risk distribution and model evaluation results.
  • Support export of analysis results.

Operating System: Ubuntu Linux

Ubuntu provides the main application-analysis and machine-learning environment.

Purpose
  • Run Python and the selected security tools.
  • Execute SBOM and vulnerability analysis.
  • Host the database and dashboard.

Security Testing Platform: Kali Linux

Kali Linux provides the controlled security-testing environment within the laboratory.

Purpose
  • Support authorized testing of the local analysis environment.
  • Validate access to the intended test services where required.
  • Assist with controlled security verification.

Virtualization Platform: VirtualBox

VirtualBox provides the isolated virtual-machine environment.

Purpose
  • Host Ubuntu and Kali Linux.
  • Separate the analysis environment from the testing environment.
  • Support repeatable laboratory experiments.

Process

STEP 01

Prepare the Isolated Laboratory Environment

  • Create the laboratory using VirtualBox.
  • Configure Ubuntu Linux as the primary application-analysis and machine-learning environment
  • Configure Kali Linux as the security-testing environment where required.
  • Establish the intended laboratory network connectivity.
  • Verify that the target application and analysis tools can operate within the authorized environment.
  • Confirm that testing is restricted to the laboratory.
Tools: VirtualBox + Ubuntu Linux + Kali Linux
STEP 02

Deploy the Mattermost Application

  • Obtain a supported Mattermost version and deploy it according to its documented requirements.
  • Configure the application in the isolated laboratory environment.
  • Start the required application services.
  • Verify that Mattermost is operational.
  • Record the application version and deployment configuration.
  • Preserve the version information for later dependency analysis.
Tools: Mattermost + Ubuntu Linux + VirtualBox
STEP 03

Identify the Available Dependency Artifacts

  • Inspect the Mattermost source repository or supported build artifacts.
  • Identify the dependency files and package-management metadata available for the selected version.
  • Determine which dependency information can be extracted reliably.
  • Record the software version and the source of the dependency artifacts.
  • Verify that the selected artifacts are appropriate for SBOM generation.
Tools: Mattermost + Python + Ubuntu Linux
STEP 04

Generate the SBOM

  • Use Syft to generate an SBOM from the supported Mattermost artifacts.
  • Select a compatible output format, such as CycloneDX or SPDX.
  • Store the generated SBOM in the project directory.
  • Verify that the SBOM contains component identifiers and version information where available.
  • Record the generation tool and input artifact details.
  • Do not assume that every SBOM includes complete dependency relationships or every transitive component.
Tools: Syft + Mattermost + Ubuntu Linux
STEP 05

Inspect the SBOM Components

  • Load the generated SBOM into the Python environment
  • Extract component names, versions, package identifiers, and available metadata.
  • Inspect the component records for missing or inconsistent information.
  • Determine which components can be matched reliably with public vulnerability records.
  • Preserve the original SBOM separately from the processed component data.
Tools: Python + Pandas + Syft
STEP 06

Obtain Public Vulnerability Records

  • Obtain the relevant public vulnerability records from OSV.dev or another suitable public source.
  • Review the available identifiers, affected package ecosystems, vulnerable version ranges, and other supported metadata.
  • Preserve the original source information and retrieval date.
  • Determine which records can be matched with the selected Mattermost components.
  • Do not assume that the public source contains every vulnerability affecting every component.
Tools: OSV.dev + Python + Pandas
STEP 07

Match Components with Known Vulnerabilities

  • Use OSV-Scanner to examine the supported dependency information or generated SBOM.
  • Identify known vulnerability matches.
  • Where necessary, compare package identifiers and version information with the corresponding public vulnerability records.
  • Record the component, matched vulnerability identifier, affected-version information, and available evidence.
  • Keep unmatched components separate from components with confirmed vulnerability matches.
Tools: OSV-Scanner + Python + Pandas
STEP 08

Cross-Check Selected Vulnerability Findings

  • Use Grype as an optional additional scanner on the supported SBOM.
  • Compare selected findings with OSV-Scanner results and available vulnerability records.
  • Investigate differences caused by data-source coverage, package identification, version interpretation, or scanner support.
  • Preserve the scanner outputs.
  • Avoid treating a scanner disagreement as automatic proof that either result is correct or incorrect.
Tools: Grype + OSV-Scanner + Python
STEP 09

Prepare the Existing Training Dataset

  • Identify an existing public dataset containing suitable software component features and risk labels.
  • Review the dataset documentation and usage conditions
  • Determine whether its labels support the intended component-risk classification objective.
  • Inspect the feature fields and check their compatibility with the SBOM-derived component information.
  • If the dataset lacks reliable labels or relevant features, select a more suitable source or revise the classification objective before training.
  • Do not fabricate the primary training dataset.
Tools: Python + Pandas + NumPy
STEP 10

Define the Risk Classification Labels

  • Determine the risk categories supported by the verified training data.
  • Use the dataset's established labels where appropriate.
  • If the project defines new categories using explicit, documented rules, apply those rules consistently and explain how the labels were derived.
  • Avoid treating all packages with a known vulnerability as automatically high-risk without considering the defined classification objective.
  • Record the class distribution and label-generation procedure.
Tools: Python + Pandas + Scikit-learn
STEP 11

Preprocess the Training Data

  • Clean unusable records and handle missing values according to the selected features.
  • Normalize compatible package identifiers and metadata.
  • Encode suitable categorical features.
  • Prepare numerical features and apply appropriate scaling where required by SVM.
  • Separate the target labels from the input features.
  • Preserve the preprocessing configuration for later inference.
Tools: Python + Pandas + NumPy + Scikit-learn
STEP 12

Train the SVM Classifier

  • Configure an SVM classifier using Scikit-learn.
  • Prepare the training and validation partitions.
  • Train the model using the verified features and labels.
  • Use suitable preprocessing and scaling within a consistent training pipeline.
  • Tune model parameters using training and validation data where appropriate.
  • Save the trained model and its preprocessing configuration.
Tools: Python + Scikit-learn
STEP 13

Evaluate the Trained Model

  • Evaluate the trained SVM using held-out test records.
  • Compare predictions with the known test labels.
  • Calculate precision, recall, F1-score, and other appropriate classification metrics.
  • Generate a confusion matrix to review classification errors.
  • Inspect false-positive and false-negative predictions.
  • Record the actual results instead of assuming that the model achieves a particular accuracy.
Tools: Python + Scikit-learn + Pandas
STEP 14

Prepare Mattermost Component Features

  • Select the Mattermost SBOM component records that contain the required information.
  • Transform compatible component metadata into the feature representation expected by the trained SVM.
  • Use the same fitted preprocessing configuration established during training.
  • Do not fit a new feature transformation independently on the Mattermost records.
  • Verify that the feature columns and ordering are compatible with the trained model.
  • Document any mismatch between the public training dataset and the application-derived records.
Tools: Python + Pandas + NumPy + Scikit-learn
STEP 15

Perform Component Risk Classification

  • Load the trained SVM model and its preprocessing pipeline.
  • Provide the compatible Mattermost component features to the model.
  • Generate the predicted risk category for each supported component.
  • Store the prediction with the corresponding package name and version.
  • Keep model predictions separate from known vulnerability matches.
  • Identify records that cannot be classified reliably because required features are missing or incompatible.
Tools: Python + Scikit-learn + Pandas
STEP 16

Validate the Component Risk Results

  • Compare the model's classifications with available vulnerability evidence.
  • Review components with known vulnerability matches and examine whether their predicted categories are consistent with the defined classification objective.
  • Investigate discrepancies between scanner findings and model predictions.
  • Use the public dataset's reference labels or other defensible validation evidence where available.
  • Do not interpret an AI-predicted high-risk category as proof of a vulnerability.
  • Record the verified findings and unresolved cases.
Tools: Python + OSV-Scanner + Pandas + Scikit-learn
STEP 17

Store Results and Create the Dashboard

  • Create a SQLite database to store component information, vulnerability matches, model predictions, and evaluation results.
  • Build a Streamlit dashboard that displays the Mattermost component inventory and associated security findings.
  • Provide separate views for known vulnerability records and SVM-predicted risk categories.
  • Add a risk-distribution chart and a component-level details table.
  • Display actual model evaluation metrics and allow analysis results to be exported.
  • Verify that the dashboard accurately reflects the stored evidence and does not present unverified predictions as confirmed vulnerabilities.
Tools: Python + SQLite + Streamlit + Pandas
STEP 18

Perform Final Validation and Security Review

  • Repeat the workflow using the selected Mattermost artifacts and the documented analysis configuration.
  • Verify the SBOM component inventory and vulnerability-matching results.
  • Review the SVM predictions and actual evaluation metrics.
  • Check the dashboard against the underlying database records.
  • Document dataset limitations, unsupported components, scanner disagreements, and model errors.
  • Record the known vulnerability evidence separately from predicted component risk.
  • Finalize the software supply-chain risk assessment with reproducible results and recommendations for further investigation.
Tools: Mattermost + Syft + OSV-Scanner + Grype + Python + Scikit-learn + SQLite + Streamlit

Outcome

Mattermost is deployed within an isolated laboratory environment as the existing application used for software supply-chain risk analysis.

An SBOM is generated from supported Mattermost artifacts, providing a structured inventory of the software components identified by the selected tool.

Component names, versions, package identifiers, and other available metadata are extracted and processed using Python.

Public vulnerability records are collected and correlated with compatible SBOM components using OSV.dev and OSV-Scanner.

An existing public dataset with suitable component features and verified labels is prepared for SVM-based risk classification

The SVM classifier is trained and evaluated using held-out data, producing measurable classification results based on the available ground truth.

Compatible Mattermost component records are processed using the same feature pipeline and classified according to the model's supported risk categories.

Known vulnerability findings and AI-predicted risk categories are maintained separately, supporting evidence-based verification and component prioritization.

SQLite stores the component inventory, vulnerability matches, model predictions, and evaluation results, while Streamlit presents the information through an interactive security dashboard.

The complete workflow demonstrates how SBOM analysis, public vulnerability records, SVM-based component classification, cybersecurity scanning tools, and dashboard reporting can support software supply-chain risk assessment while documenting data compatibility requirements and model limitations.