README.md

---
language:
- en
license: apache-2.0
task_categories:
- graph-ml
tags:
- knowledge-graph
- cybersecurity
- mitre-attack
- capec
- cwe
- cve
- cpe
- d3fend
- atlas
- car
- engage
- f3
- epss
- kev
- vulnrichment
- ghsa
- sigma
- exploitdb
- misp-galaxy
- lolbas
- loldrivers
- atomic-red-team
- nist-800-53
- nuclei
- euvd
- osv
- stix
- threat-intelligence
- triples
pretty_name: "Security Knowledge Graph Triples (ATT&CK / CAPEC / CWE / CVE / CPE / D3FEND / ATLAS / CAR / ENGAGE / F3 / EPSS / KEV / Vulnrichment / GHSA / Sigma / ExploitDB / MISP Galaxies / LOLBAS / LOLDrivers / Atomic Red Team / NIST 800-53 / Nuclei / EUVD / OSV)"
size_categories:
- 10M<n<100M
configs:
- config_name: enterprise
  data_files:
  - split: train
    path: data/enterprise.parquet
  default: true
- config_name: mobile
  data_files:
  - split: train
    path: data/mobile.parquet
- config_name: ics
  data_files:
  - split: train
    path: data/ics.parquet
- config_name: attack-all
  data_files:
  - split: train
    path: data/attack-all.parquet
- config_name: capec
  data_files:
  - split: train
    path: data/capec.parquet
- config_name: cwe
  data_files:
  - split: train
    path: data/cwe.parquet
- config_name: cve
  data_files:
  - split: train
    path: data/cve.parquet
- config_name: cpe
  data_files:
  - split: train
    path: data/cpe.parquet
- config_name: d3fend
  data_files:
  - split: train
    path: data/d3fend.parquet
- config_name: atlas
  data_files:
  - split: train
    path: data/atlas.parquet
- config_name: car
  data_files:
  - split: train
    path: data/car.parquet
- config_name: engage
  data_files:
  - split: train
    path: data/engage.parquet
- config_name: f3
  data_files:
  - split: train
    path: data/f3.parquet
- config_name: epss
  data_files:
  - split: train
    path: data/epss.parquet
- config_name: kev
  data_files:
  - split: train
    path: data/kev.parquet
- config_name: vulnrichment
  data_files:
  - split: train
    path: data/vulnrichment.parquet
- config_name: ghsa
  data_files:
  - split: train
    path: data/ghsa.parquet
- config_name: sigma
  data_files:
  - split: train
    path: data/sigma.parquet
- config_name: exploitdb
  data_files:
  - split: train
    path: data/exploitdb.parquet
- config_name: misp_galaxy
  data_files:
  - split: train
    path: data/misp_galaxy.parquet
- config_name: lolbas
  data_files:
  - split: train
    path: data/lolbas.parquet
- config_name: loldrivers
  data_files:
  - split: train
    path: data/loldrivers.parquet
- config_name: atomic
  data_files:
  - split: train
    path: data/atomic.parquet
- config_name: nist_800_53
  data_files:
  - split: train
    path: data/nist_800_53.parquet
- config_name: nuclei
  data_files:
  - split: train
    path: data/nuclei.parquet
- config_name: euvd
  data_files:
  - split: train
    path: data/euvd.parquet
- config_name: osv
  data_files:
  - split: train
    path: data/osv.parquet
- config_name: combined
  data_files:
  - split: train
    path: data/combined.parquet
dataset_info:
  features:
  - name: subject
    dtype: string
  - name: predicate
    dtype: string
  - name: object
    dtype: string
  - name: source
    dtype: string
  - name: object_type
    dtype: string
  - name: meta
    dtype: string
---

# Security Knowledge Graph Triples

Security data from 24 sources represented as **Subject-Predicate-Object (SPO) triples** in Parquet format, ready for knowledge-graph construction, graph-ML, RAG pipelines, and threat-intelligence analysis.

Sources: [ATT&CK](https://attack.mitre.org/) · [CAPEC](https://capec.mitre.org/) · [CWE](https://cwe.mitre.org/) · [CVE](https://www.cve.org/) · [CPE](https://nvd.nist.gov/products/cpe) · [D3FEND](https://d3fend.mitre.org/) · [ATLAS](https://atlas.mitre.org/) · [CAR](https://car.mitre.org/) · [ENGAGE](https://engage.mitre.org/) · [F3](https://ctid.mitre.org/fraud) · [EPSS](https://www.first.org/epss/) · [KEV](https://www.cisa.gov/known-exploited-vulnerabilities-catalog) · [Vulnrichment](https://github.com/cisagov/vulnrichment) · [GHSA](https://github.com/github/advisory-database) · [Sigma](https://github.com/SigmaHQ/sigma) · [ExploitDB](https://gitlab.com/exploit-database/exploitdb) · [MISP Galaxies](https://github.com/MISP/misp-galaxy) · [LOLBAS](https://lolbas-project.github.io/) · [LOLDrivers](https://www.loldrivers.io/) · [Atomic Red Team](https://github.com/redcanaryco/atomic-red-team) · [NIST 800-53](https://github.com/center-for-threat-informed-defense/mappings-explorer) · [Nuclei](https://github.com/projectdiscovery/nuclei-templates) · [EUVD](https://euvd.enisa.europa.eu/) · [OSV](https://osv.dev/)

*Last updated: 2026-07-13T09:12:52Z*

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("s0u9ata/security-kg", "enterprise")
print(ds["train"][0])
# {'subject': 'T1059.001', 'predicate': 'rdf:type', 'object': 'Technique', 'source': 'attack', 'object_type': 'enum', 'meta': ''}
```

## Configurations

| Config | Description | Est. Triples | Status |
|--------|-------------|-------------|--------|
| `enterprise` (default) | Enterprise ATT&CK | 43,739 | Current |
| `mobile` | Mobile ATT&CK | 5,579 | Current |
| `ics` | ICS ATT&CK | 4,266 | Current |
| `attack-all` | ATT&CK combined (deduplicated) | 51,907 | Current |
| `capec` | CAPEC attack patterns | 8,114 | Current |
| `cwe` | CWE weaknesses | 14,583 | Current |
| `cve` | CVE vulnerabilities | 3,875,509 | Current |
| `cpe` | CPE platform enumeration | 13,545,566 | Last good version |
| `d3fend` | D3FEND defensive techniques | 8,154 | Current |
| `atlas` | ATLAS AI/ML techniques | 1,373 | Current |
| `car` | CAR analytics | 1,617 | Current |
| `engage` | ENGAGE adversary engagement | 1,464 | Current |
| `f3` | F3 fraud techniques & tactics | 1,053 | Current |
| `epss` | EPSS exploit prediction scores | 694,384 | Current |
| `kev` | KEV known exploited vulns | 17,938 | Current |
| `vulnrichment` | CISA Vulnrichment (SSVC, CVSS, CWE enrichment) | 1,549,549 | Current |
| `ghsa` | GitHub Security Advisories | 382,938 | Current |
| `sigma` | Sigma detection rules | 33,498 | Current |
| `exploitdb` | ExploitDB public exploits | 347,263 | Current |
| `misp_galaxy` | MISP Galaxy threat intelligence clusters | 203,459 | Current |
| `lolbas` | LOLBAS living-off-the-land binaries | 3,692 | Current |
| `loldrivers` | LOLDrivers vulnerable/malicious drivers | 11,738 | Current |
| `atomic` | Atomic Red Team test definitions | 11,024 | Current |
| `nist_800_53` | NIST 800-53 → ATT&CK control mappings | 4,786 | Current |
| `nuclei` | Nuclei vulnerability detection templates | 98,515 | Current |
| `euvd` | EUVD European vulnerability database | 4,923 | Current |
| `osv` | OSV open-source vulnerabilities (23 ecosystems) | 7,202,861 | Current |
| `combined` | All sources merged (deduplicated) | 28,075,908 | Current |




<!-- fallback-status-note -->
> **Note:** `cpe` failed conversion and uses its last known good version. The `combined` config includes this fallback version.

## Knowledge Graph Structure

```
     Group  Campaign
        \      /
          uses
           |
           v
      TECHNIQUE -----> Tactic
        ^  ^  ^
        |  |  |
        |  |  +-- D3FEND (counters)
        |  |  +-- CAR (detects)
        |  |  +-- Sigma (detects)
        |  |  +-- ENGAGE (engages)
        |  |  +-- F3 (fraud techniques)
        |  |  +-- ATLAS (related)
        |  |  +-- MISP Galaxies (cross-refs)
        |  |  +-- LOLBAS (maps-to)
        |  |  +-- LOLDrivers (maps-to)
        |  |  +-- Atomic Red Team (tests)
        |  |  +-- NIST 800-53 (mitigates)
        |  |
        |  +-- Mitigation (mitigates)
        |  +-- DataComponent (detects)
        |
        +-- maps-to -- CAPEC
                         |
                  related-weakness
                         |
                         v
                        CWE
                         ^
                         |
                  related-weakness
                         |
                        CVE ----> CPE
                         ^
                         |
                   EPSS (score)
                   KEV (exploited)
                   GHSA (advisory)
                   Vulnrichment (SSVC)
                   ExploitDB (exploit)
                   Nuclei (detection template)
                   EUVD (EU advisory)
                   OSV (open-source vuln)
```

## Schema

Each row is an enriched triple with six string columns:

| Column | Description | Examples |
|--------|-------------|----------|
| `subject` | Entity ID | `T1059.001`, `G0016`, `CAPEC-66`, `CWE-79`, `CVE-2024-1234`, `cpe:2.3:a:apache:httpd:*`, `D3-FE`, `AML.T0000`, `CAR-2024-01-001`, `EAC0001`, `GHSA-xxxx-yyyy-zzzz`, `EDB-16929`, `Msbuild.exe`, `EUVD-2025-4893`, `AC-2`, `PYSEC-2024-1234` |
| `predicate` | Property name or relationship type | `rdf:type`, `name`, `uses`, `mitigates`, `epss-score`, `counters`, `ssvc-exploitation`, `exploits-cve`, `detects-technique` |
| `object` | Value or target entity ID | `Technique`, `PowerShell`, `T1059`, `CWE-89`, `0.97500`, `SecurityAdvisory`, `SigmaRule`, `Exploit` |
| `source` | Originating dataset | `attack`, `cve`, `cwe`, `capec`, `epss`, `kev`, `ghsa`, `sigma`, `d3fend`, `atlas`, `car`, `engage`, `f3`, `cpe`, `vulnrichment`, `exploitdb`, `misp_galaxy`, `lolbas`, `loldrivers`, `atomic`, `nist_800_53`, `nuclei`, `euvd`, `osv` |
| `object_type` | Value type of the object | `string`, `id`, `enum`, `date`, `number`, `boolean`, `url` |
| `meta` | Supplemental JSON metadata (empty string if none) | `{"references":["https://..."],"credits":[...]}`, `{"cvss_vector":"...","cvss_version":"3.1"}` |

## Predicate Reference

### ATT&CK Entity Properties

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | Entity type | `Technique`, `Group`, `Malware`, `Tool`, `Tactic`, `Mitigation`, `Campaign`, `DataSource`, `DataComponent` |
| `name` | Display name | `PowerShell` |
| `description` | Full description text | `Adversaries may abuse PowerShell...` |
| `platform` | Applicable platform | `Windows`, `Linux`, `macOS` |
| `domain` | ATT&CK domain | `enterprise-attack` |
| `alias` | Alternative name | `Cozy Bear` |
| `is-subtechnique` | Whether entity is a sub-technique | `True`, `False` |
| `belongs-to-tactic` | Tactic ATT&CK ID | `TA0002` |
| `shortname` | Tactic shortname | `credential-access` |
| `url` | ATT&CK website URL | `https://attack.mitre.org/techniques/T1059/001` |
| `created` / `modified` | Timestamps | `2020-01-14 17:18:32...` |

### ATT&CK Relationship Predicates

| Predicate | Typical subject / object | Example |
|-----------|--------------------------|---------|
| `uses` | Group/Campaign/Software / Technique | `G0016 / T1059.001` |
| `mitigates` | Mitigation / Technique | `M1049 / T1059.001` |
| `subtechnique-of` | Sub-technique / Parent technique | `T1059.001 / T1059` |
| `detects` | DataComponent / Technique | `DC0001 / T1059.001` |
| `attributed-to` | Campaign / Group | `C0018 / G0016` |

### CAPEC Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `AttackPattern` | `AttackPattern` |
| `name` / `description` | Display name / full text | `SQL Injection` |
| `abstraction` / `status` | Level / status | `Standard`, `Stable` |
| `likelihood` / `severity` | Attack likelihood / severity | `High` |
| `child-of` | Parent attack pattern | `CAPEC-248` |
| `related-weakness` | Related CWE | `CWE-89` |
| `maps-to-technique` | Mapped ATT&CK technique | `T1190.002` |

### CWE Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `Weakness` | `Weakness` |
| `name` / `description` | Display name / full text | `Cross-site Scripting (XSS)` |
| `abstraction` / `status` | Level / status | `Base`, `Stable` |
| `likelihood-of-exploit` | Exploitation likelihood | `High` |
| `child-of` | Parent weakness | `CWE-74` |
| `related-attack-pattern` | Related CAPEC | `CAPEC-86` |
| `platform` | Applicable platform | `JavaScript` |
| `consequence-scope` / `consequence-impact` | Impact | `Confidentiality`, `Read Data` |
| `introduction-phase` | Introduction phase | `Implementation` |

### CVE Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `Vulnerability` | `Vulnerability` |
| `state` | CVE state | `PUBLISHED` |
| `description` | English description | `A remote code execution...` |
| `date-published` / `date-updated` | Timestamps | `2024-01-15T00:00:00.000Z` |
| `assigner` | Assigning organization | `microsoft` |
| `vendor` / `product` | Affected vendor/product | `Microsoft`, `Windows` |
| `affects-cpe` | Affected CPE string | `cpe:2.3:o:microsoft:windows_10:*` |
| `platform` | Affected platform | `x64` |
| `related-weakness` | Related CWE | `CWE-79` |
| `cvss-base-score` / `cvss-severity` | CVSS metrics | `9.8`, `CRITICAL` |

### CPE Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `Platform` | `Platform` |
| `part` | CPE part type | `application`, `operating_system`, `hardware` |
| `vendor` / `product` / `version` | Components | `apache`, `httpd`, `2.4.51` |
| `title` | English display name | `Apache HTTP Server 2.4.51` |
| `created` / `modified` | Timestamps | `2021-10-07` |

### D3FEND Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `DefensiveTechnique` or `OffensiveTechnique` | `DefensiveTechnique` |
| `name` / `definition` | Display name / definition | `File Encryption` |
| `synonym` | Alternative name | `Disk Encryption` |
| `child-of` | Parent technique | `PlatformHardening` |
| `counters` | Countered offensive technique | `T1059` |

### ATLAS Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `Tactic`, `Technique`, `CaseStudy`, `Mitigation` | `Technique` |
| `name` / `description` | Display name / full text | `ML Supply Chain Compromise` |
| `maturity` | Technique maturity | `Reviewed` |
| `belongs-to-tactic` | Parent tactic | `AML.TA0001` |
| `subtechnique-of` | Parent technique | `AML.T0000` |
| `related-attack-technique` | Linked ATT&CK technique | `T1195` |
| `related-attack-tactic` | Linked ATT&CK tactic | `TA0001` |
| `uses-technique` | Case study technique | `AML.T0000` |
| `mitigates` | Mitigated technique | `AML.T0000` |

### CAR Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `Analytic` | `Analytic` |
| `title` / `description` | Analytic name / full text | `Suspicious PowerShell Commands` |
| `platform` | Applicable platform | `Windows` |
| `information-domain` | Information domain | `Host` |
| `analytic-type` | Type of analytic | `Situational Awareness` |
| `detects-technique` | Detected ATT&CK technique | `T1059` |
| `detects-subtechnique` | Detected subtechnique | `T1059.001` |
| `covers-tactic` | Covered ATT&CK tactic | `Execution` |
| `maps-to-d3fend` | Linked D3FEND technique | `D3-PSA` |

### ENGAGE Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `EngagementActivity` or `AdversaryVulnerability` | `EngagementActivity` |
| `name` / `description` | Display name / full text | `Software Manipulation` |
| `engages-technique` | Engaged ATT&CK technique | `T1001` |
| `vulnerability-of` | ATT&CK technique this adversary vulnerability applies to | `T1001` |
| `addresses-vulnerability` | Addressed adversary vulnerability | `EAV0001` |

### F3 Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `Tactic` or `Technique` | `Technique` |
| `name` / `description` | Display name / full text | `Account Takeover` |
| `shortname` | Tactic shortname | `positioning`, `monetization` |
| `is-subtechnique` | Whether entity is a sub-technique | `true` |
| `belongs-to-tactic` | Parent tactic | `FA0001` |
| `subtechnique-of` | Parent technique | `F1001` |
| `url` | F3 website URL | `https://ctid.mitre.org/fraud/techniques/F1001` |
| `created` / `modified` | Timestamps | `2026-04-02T19:15:57.686Z` |

### EPSS Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `epss-score` | Exploit probability (0-1) | `0.97500` |
| `epss-percentile` | Score percentile (0-1) | `0.99900` |

### KEV Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `KnownExploitedVulnerability` | `KnownExploitedVulnerability` |
| `kev-vendor` / `kev-product` | Affected vendor/product | `Microsoft`, `Windows` |
| `kev-name` / `kev-description` | Vulnerability name/description | `Windows Privilege Escalation` |
| `kev-date-added` / `kev-due-date` | Dates | `2024-01-15` |
| `kev-required-action` | Required remediation action | `Apply updates per vendor instructions.` |
| `kev-ransomware-use` | Ransomware campaign use | `Known`, `Unknown` |
| `related-weakness` | Related CWE | `CWE-269` |

### Vulnrichment Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `ssvc-exploitation` | SSVC exploitation status | `active`, `poc`, `none` |
| `ssvc-automatable` | Whether exploitation is automatable | `yes`, `no` |
| `ssvc-technical-impact` | Technical impact level | `total`, `partial` |
| `adp-cvss-base-score` | CISA-analyzed CVSS base score | `9.8` |
| `adp-cvss-severity` | CISA-analyzed CVSS severity | `CRITICAL` |
| `adp-related-weakness` | CISA-assigned CWE | `CWE-79` |
| `adp-affects-cpe` | CISA-assigned CPE | `cpe:2.3:o:microsoft:windows_10:*` |

### GHSA Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `SecurityAdvisory` | `SecurityAdvisory` |
| `summary` | Advisory summary | `XSS vulnerability in example-package` |
| `date-published` / `date-modified` | Timestamps | `2024-01-15T00:00:00Z` |
| `severity` | Severity level | `HIGH`, `MODERATE`, `LOW`, `CRITICAL` |
| `related-cve` | Associated CVE | `CVE-2024-1234` |
| `related-weakness` | Associated CWE | `CWE-79` |
| `cvss-vector` | CVSS v3 vector string | `CVSS:3.1/AV:N/AC:L/...` |
| `affects-package` | Affected package (ecosystem/name) | `npm/example-package` |
| `fixed-in` | Fixed version for package (ecosystem/name@version) | `npm/example-package@2.0.1` |

### Sigma Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `SigmaRule` | `SigmaRule` |
| `title` / `description` | Rule name / full text | `Suspicious PowerShell Download` |
| `status` | Rule maturity | `stable`, `test`, `experimental` |
| `level` | Detection severity | `critical`, `high`, `medium`, `low`, `informational` |
| `author` / `date` | Rule author / creation date | `Security Researcher`, `2024-01-15` |
| `logsource-category` | Log source category | `process_creation`, `network_connection` |
| `logsource-product` | Log source product | `windows`, `linux` |
| `logsource-service` | Log source service | `sshd`, `sysmon` |
| `detects-technique` | Detected ATT&CK technique | `T1059.001` |
| `related-cve` | Related CVE | `CVE-2024-1234` |

### ExploitDB Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `Exploit` | `Exploit` |
| `description` | Exploit description | `Apache HTTP Server RCE` |
| `date-published` | Publication date | `2024-01-15` |
| `author` | Exploit author | `Metasploit` |
| `exploit-type` | Exploit category | `remote`, `local`, `dos`, `webapps` |
| `platform` | Target platform | `linux`, `windows`, `aix` |
| `verified` | Verified by OffSec | `True` |
| `exploits-cve` | Exploited CVE | `CVE-2024-1234` |

### MISP Galaxy Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | Galaxy entity type | `ThreatActor`, `Ransomware`, `Botnet`, `RAT` |
| `name` | Display name | `APT1` |
| `description` | Full description | (text) |
| `galaxy` | Galaxy cluster type | `threat-actor`, `ransomware` |
| `synonym` | Alternative name | `Comment Crew` |
| `country` | Country code (ISO 3166-1) | `CN` |
| `cfr-suspected-state-sponsor` | Suspected state sponsor | `China` |
| `targets-country` | Targeted country | `United States` |
| `targets-sector` | Targeted sector | `Government` |
| `attribution-confidence` | Confidence level | `50` |
| `similar-to` | Similar/duplicate entity | `misp:<uuid>` |
| `uses` | Uses technique/tool | `misp:<uuid>` |
| `used-by` | Used by actor | `misp:<uuid>` |
| `variant-of` | Variant relationship | `misp:<uuid>` |
| `targets` | Targets entity | `misp:<uuid>` |
| `attributed-to` | Attributed to entity | `misp:<uuid>` |
| `misp-related` | Generic relationship | `misp:<uuid>` |
| `related-attack-id` | Cross-link to ATT&CK | `T1059.001`, `G0006` |

### LOLBAS Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `LOLBinary` | `LOLBinary` |
| `name` | Binary display name | `Msbuild.exe` |
| `description` | Binary description | `Used to compile and execute code` |
| `maps-to-technique` | Mapped ATT&CK technique | `T1127.001` |
| `category` | Use category | `Execute`, `Download` |
| `usecase` | Use case description | `Compile and run code` |
| `privileges` | Required privileges | `User` |
| `platform` | Target platform | `Windows` |
| `full-path` | File system path | `C:\Windows\Microsoft.NET\...` |

### LOLDrivers Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `LOLDriver` | `LOLDriver` |
| `name` | Driver name | `RTCore64.sys` |
| `category` | Driver category | `vulnerable driver`, `malicious driver` |
| `maps-to-technique` | Mapped ATT&CK technique | `T1068` |
| `usecase` | Use case description | `Exploits` |
| `privileges` | Required privileges | `kernel` |
| `platform` | Target platform | `Windows` |
| `sha256` / `sha1` / `md5` | Sample hashes | `01...af` |
| `vendor` / `product` | Driver vendor/product | `Micro-Star Int'l Co.`, `RTCore64` |

### Atomic Red Team Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `AtomicTest` | `AtomicTest` |
| `name` | Test name | `Mimikatz - Cred Dump` |
| `description` | Test description | `Runs Mimikatz to dump credentials` |
| `tests-technique` | Tested ATT&CK technique | `T1003.001` |
| `platform` | Supported platform | `windows`, `linux`, `macos` |
| `executor` | Execution method | `powershell`, `sh`, `command_prompt` |

### NIST 800-53 Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `SecurityControl` | `SecurityControl` |
| `name` | Control name | `Account Management` |
| `description` | Control description | `Manage system accounts...` |
| `control-family` | Control family | `AC`, `SI`, `AU` |
| `mitigates-technique` | Mitigated ATT&CK technique | `T1078` |

### Nuclei Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `NucleiTemplate` | `NucleiTemplate` |
| `name` | Template name | `Apache Struts2 RCE` |
| `description` | Template description | `Detects Apache Struts2 RCE...` |
| `severity` | Detection severity | `critical`, `high`, `medium`, `low`, `info` |
| `author` | Template author | `pdteam` |
| `related-weakness` | Related CWE | `CWE-94` |
| `related-cve` | Related CVE | `CVE-2023-1234` |
| `cvss-base-score` | CVSS base score | `9.8` |
| `cvss-vector` | CVSS vector string | `CVSS:3.1/AV:N/AC:L/...` |

### EUVD Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `EUVulnerability` | `EUVulnerability` |
| `description` | Vulnerability description | `A remote code execution...` |
| `date-published` | Publication date | `2025-01-15` |
| `cvss-base-score` | CVSS base score | `9.8` |
| `cvss-vector` | CVSS vector string | `CVSS:3.1/AV:N/AC:L/...` |
| `epss-score` | EPSS score | `0.95` |
| `related-cve` | Related CVE | `CVE-2025-1234` |
| `vendor` / `product` | Affected vendor/product | `Apache`, `HTTP Server` |

### OSV Predicates

| Predicate | Description | Example object value |
|-----------|-------------|---------------------|
| `rdf:type` | `OSVulnerability` | `OSVulnerability` |
| `summary` | Vulnerability summary | `XSS in example-package` |
| `date-published` / `date-modified` | Timestamps | `2024-01-15T00:00:00Z` |
| `related-cve` | Related CVE | `CVE-2024-1234` |
| `related-weakness` | Related CWE | `CWE-79` |
| `affects-package` | Affected package (ecosystem/name) | `PyPI/requests` |
| `ecosystem` | Package ecosystem | `PyPI`, `npm`, `Go`, `crates.io` |
| `cvss-vector` | CVSS vector string | `CVSS:3.1/AV:N/AC:L/...` |

## Dataset Creation

### Source Data

| Source | Feed | Format |
|--------|------|--------|
| ATT&CK | [`mitre-attack/attack-stix-data`](https://github.com/mitre-attack/attack-stix-data) | STIX 2.0 JSON |
| CAPEC | [`capec_latest.xml`](https://capec.mitre.org/data/xml/capec_latest.xml) | XML |
| CWE | [`cwec_latest.xml.zip`](https://cwe.mitre.org/data/xml/cwec_latest.xml.zip) | XML (ZIP) |
| CVE | [`CVEProject/cvelistV5`](https://github.com/CVEProject/cvelistV5/releases) | JSON 5.x (ZIP) |
| CPE | [`nvdcpe-2.0.tar.gz`](https://nvd.nist.gov/feeds/json/cpe/2.0/nvdcpe-2.0.tar.gz) | JSON (tar.gz) |
| D3FEND | [`d3fend.json`](https://d3fend.mitre.org/ontologies/d3fend.json) | JSON-LD |
| ATLAS | [`ATLAS.yaml`](https://raw.githubusercontent.com/mitre-atlas/atlas-data/main/dist/ATLAS.yaml) | YAML |
| CAR | [`mitre-attack/car`](https://github.com/mitre-attack/car) | YAML (ZIP) |
| ENGAGE | [`attack_mapping.json`](https://raw.githubusercontent.com/mitre/engage/main/Data/json/attack_mapping.json) | JSON |
| F3 | [`fight-fraud-framework`](https://github.com/center-for-threat-informed-defense/fight-fraud-framework) | STIX 2.1 JSON |
| EPSS | [`epss_scores-current.csv.gz`](https://epss.cyentia.com/epss_scores-current.csv.gz) | CSV (gzip) |
| KEV | [`known_exploited_vulnerabilities.json`](https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json) | JSON |
| Vulnrichment | [`cisagov/vulnrichment`](https://github.com/cisagov/vulnrichment) | JSON 5.x (ZIP) |
| GHSA | [`github/advisory-database`](https://github.com/github/advisory-database) | OSV JSON (ZIP) |
| Sigma | [`SigmaHQ/sigma`](https://github.com/SigmaHQ/sigma/releases) | YAML (ZIP) |
| ExploitDB | [`files_exploits.csv`](https://gitlab.com/exploit-database/exploitdb/-/raw/main/files_exploits.csv) | CSV |
| MISP Galaxies | [`MISP/misp-galaxy`](https://github.com/MISP/misp-galaxy) | JSON (ZIP) |
| LOLBAS | [`LOLBAS-Project/LOLBAS`](https://github.com/LOLBAS-Project/LOLBAS) | YAML (ZIP) |
| LOLDrivers | [`magicsword-io/LOLDrivers`](https://github.com/magicsword-io/LOLDrivers) | YAML (ZIP) |
| Atomic Red Team | [`redcanaryco/atomic-red-team`](https://github.com/redcanaryco/atomic-red-team) | YAML (ZIP) |
| NIST 800-53 | [`mappings-explorer`](https://github.com/center-for-threat-informed-defense/mappings-explorer) | JSON (ZIP) |
| Nuclei | [`projectdiscovery/nuclei-templates`](https://github.com/projectdiscovery/nuclei-templates) | YAML (ZIP) |
| EUVD | [`euvdservices.enisa.europa.eu`](https://euvdservices.enisa.europa.eu/api/kev/dump) | JSON |
| OSV | [`osv.dev`](https://osv.dev/) | JSON (per-ecosystem ZIPs) |

### Conversion Pipeline

The converter downloads source data, extracts entity property triples and relationship triples, and writes them as Parquet files. The source code and full documentation are at:

**[github.com/S0UGATA/security-kg](https://github.com/S0UGATA/security-kg)**

To regenerate or update this dataset:

```bash
git clone https://github.com/S0UGATA/security-kg.git
cd security-kg
pip install -r requirements.txt
python src/convert.py
```

This produces fresh Parquet files in `output/` from the latest data across all 24 sources.

## Visualizer

Explore the Parquet files interactively at [security-kg-viz](https://s0ugata.github.io/security-kg-viz/).

### Pre-computed neighborhoods (`neighborhoods/`)

For the most-connected entities in `combined.parquet`, this dataset ships
pre-rendered multi-hop neighborhood JSONs the visualizer can fetch directly,
skipping the DuckDB-WASM + Parquet path entirely for hot lookups:

```
neighborhoods/
  T1059.json              # array of Triple objects (depth=2, limit=500)
  T1059.001.json
  CVE-2024-1234.json
  CAPEC-100.json
  ...
  index.json              # { source, fingerprint, depth, limit, entities: [...] }
```

Each `<entity>.json` is the same shape the viz already builds from
`q.entityNeighborhood()` — an array of `{subject, predicate, object, source,
object_type, object_canonical}`. Filenames use a reversible slug (characters
outside `[A-Za-z0-9._-]` are `_xx` hex-escaped); `index.json` is the
authoritative mapping from entity → filename and includes a parquet
fingerprint for cache invalidation. The bundle is regenerated each weekly
refresh whenever `combined.parquet` changes.

## Use Cases

- **Knowledge Graph Construction**: Load triples into Neo4j, RDFLib, or NetworkX for graph queries
- **Graph ML**: Train graph neural networks (GNNs) on security data structure for link prediction
- **RAG / LLM Grounding**: Use triples as structured context for retrieval-augmented generation
- **Threat Intelligence**: Query relationships between groups, techniques, vulnerabilities, and mitigations
- **Vulnerability Prioritization**: Combine SSVC, EPSS, KEV, and ExploitDB data for risk-based triage
- **Defensive Gap Analysis**: Find heavily-used ATT&CK techniques with insufficient detection coverage
- **Supply Chain Risk**: Score open-source packages by linking GHSA advisories to CVE/EPSS/KEV enrichment
- **Security Automation**: Programmatically map detections to techniques to tactics

## Cross-Source Analysis Notebook

The repository includes a [Jupyter notebook](https://github.com/S0UGATA/security-kg/blob/main/examples/cross_source_visualizations.ipynb) with 16 cross-source analyses and visualizations built on `combined.parquet` — covering SSVC patch prioritization, defensive gap analysis, kill chain tactic coverage, exploit weaponization timelines, ransomware CWE pipelines, supply chain package risk, and more.

## Example Queries

### SSVC Patch Prioritization (Vulnrichment + EPSS + KEV)

```python
import pandas as pd
from datasets import load_dataset

# Load combined graph for cross-source queries
ds = load_dataset("s0u9ata/security-kg", "combined")
df = ds["train"].to_pandas()

# Build SSVC triage matrix: exploitation status × automatable × EPSS score
ssvc = df[df.predicate == "ssvc-exploitation"][["subject", "object"]].rename(columns={"object": "exploitation"})
auto = df[df.predicate == "ssvc-automatable"][["subject", "object"]].rename(columns={"object": "automatable"})
epss = df[df.predicate == "epss-score"][["subject", "object"]].copy()
epss["epss"] = epss.object.astype(float)

triage = ssvc.merge(auto, on="subject").merge(epss[["subject", "epss"]], on="subject")

# Highest priority: actively exploited + automatable + high EPSS
critical = triage[(triage.exploitation == "active") & (triage.automatable == "yes") & (triage.epss > 0.9)]
print(f"Immediate action: {len(critical)} CVEs")
```

### Defensive Gap Analysis (ATT&CK + Sigma + D3FEND + CAR)

```python
# Find ATT&CK techniques heavily used by APT groups but poorly covered by detections
uses = df[(df.predicate == "uses") & df.subject.str.startswith("G")]
group_usage = uses.groupby("object").subject.nunique().rename("groups_using")

# Count detection sources per technique (Sigma + CAR + D3FEND + ENGAGE)
sigma = df[df.predicate == "detects-technique"].groupby("object").subject.nunique().rename("detections")
d3fend = df[df.predicate == "restricts"].groupby("object").subject.nunique().rename("defenses")

coverage = pd.DataFrame(group_usage).join(sigma).join(d3fend).fillna(0)
gaps = coverage[(coverage.groups_using > 10) & (coverage.detections < 5)]
print(f"High-usage, low-detection techniques: {len(gaps)}")
```

### Supply Chain Risk (GHSA + CVE + EPSS + KEV + ExploitDB)

```python
# Score open-source packages by aggregating risk from linked CVEs
ghsa_cve = df[df.predicate == "related-cve"][["subject", "object"]].rename(columns={"subject": "ghsa", "object": "cve"})
packages = df[df.predicate == "affects-package"][["subject", "object"]].rename(columns={"subject": "ghsa", "object": "pkg"})

epss_scores = df[df.predicate == "epss-score"][["subject", "object"]].copy()
epss_scores["epss"] = epss_scores.object.astype(float)

kev_cves = set(df[(df.predicate == "rdf:type") & (df.object == "KnownExploitedVulnerability")].subject)
exploit_cves = set(df[df.predicate == "exploits-cve"].object)

# Join package → GHSA → CVE → enrichment
risk = packages.merge(ghsa_cve, on="ghsa").merge(epss_scores[["subject", "epss"]], left_on="cve", right_on="subject")
risk["in_kev"] = risk.cve.isin(kev_cves)
risk["has_exploit"] = risk.cve.isin(exploit_cves)
risk["ecosystem"] = risk.pkg.str.split("/").str[0]

# Top ecosystems by high-risk CVE count
high_risk = risk[(risk.epss > 0.5) | risk.in_kev | risk.has_exploit]
print(high_risk.groupby("ecosystem").cve.nunique().sort_values(ascending=False).head(10))
```

### CAPEC → CWE → CVE (Attack Pattern Chain)

```python
capec = load_dataset("s0u9ata/security-kg", "capec")["train"].to_pandas()
cve = load_dataset("s0u9ata/security-kg", "cve")["train"].to_pandas()

# Find CWEs related to SQL Injection (CAPEC-66)
cwe_ids = capec[(capec.subject == "CAPEC-66") & (capec.predicate == "related-weakness")].object.tolist()

# Find CVEs with those CWEs
for cwe_id in cwe_ids:
    related_cves = cve[(cve.predicate == "related-weakness") & (cve.object == cwe_id)].subject.unique()
    print(f"{cwe_id}: {len(related_cves)} CVEs")
```

### D3FEND (Defensive Taxonomy)

```python
ds = load_dataset("s0u9ata/security-kg", "d3fend")
df = ds["train"].to_pandas()

# All 497 defensive techniques in the D3FEND taxonomy
defenses = df[(df.predicate == "rdf:type") & (df.object == "DefensiveTechnique")]
print(f"Defensive techniques: {len(defenses)}")

# Find children of a category (e.g., all techniques under Network Traffic Analysis)
children = df[(df.predicate == "child-of") & (df.object == "NetworkTrafficAnalysis")].subject.tolist()

# Get their names
names = df[df.predicate == "name"][["subject", "object"]]
print(names[names.subject.isin(children)].to_string(index=False))
```

## Source Licensing & Attribution

This dataset is published under the Apache 2.0 license. The underlying source data is provided under various licenses as detailed below. By using this dataset, you agree to comply with each source's respective terms.

| Source | License | Attribution |
|--------|---------|-------------|
| [ATT&CK](https://attack.mitre.org/resources/terms-of-use/) | Custom royalty-free (MITRE) | © The MITRE Corporation. Reproduced and distributed with the permission of The MITRE Corporation. |
| [CAPEC](https://capec.mitre.org/about/termsofuse.html) | Custom royalty-free (MITRE) | © The MITRE Corporation. Reproduced and distributed with the permission of The MITRE Corporation. |
| [CWE](https://cwe.mitre.org/about/termsofuse.html) | Custom royalty-free (MITRE) | © The MITRE Corporation. Reproduced and distributed with the permission of The MITRE Corporation. |
| [CVE](https://www.cve.org/Legal/TermsOfUse) | Custom permissive (MITRE) | © The MITRE Corporation. CVE® is a registered trademark of The MITRE Corporation. |
| [CPE / NVD](https://nvd.nist.gov/developers/terms-of-use) | Public domain (NIST) | This product uses data from the NVD API but is not endorsed or certified by the NVD. |
| [D3FEND](https://github.com/d3fend/d3fend-ontology) | MIT License | © The MITRE Corporation. MITRE D3FEND™ is a trademark of The MITRE Corporation. |
| [ATLAS](https://github.com/mitre-atlas/atlas-data) | Apache 2.0 | © MITRE. |
| [CAR](https://github.com/mitre-attack/car) | Apache 2.0 | © The MITRE Corporation. |
| [ENGAGE](https://engage.mitre.org/) | Apache 2.0 ([GitHub repo](https://github.com/mitre/engage/blob/main/LICENSE.md)) / Custom restrictive ([website ToU](https://engage.mitre.org/terms-of-use/)) | © The MITRE Corporation. Reproduced and distributed with the permission of The MITRE Corporation. Note: the GitHub repo is licensed Apache 2.0, but the website terms restrict use to internal/non-commercial purposes. Clarification pending with MITRE. |
| [F3](https://github.com/center-for-threat-informed-defense/fight-fraud-framework) | Apache 2.0 | © MITRE Engenuity, Center for Threat-Informed Defense. |
| [EPSS](https://www.first.org/epss/) | Custom permissive (FIRST) | Jacobs, Romanosky, Edwards, Roytman, Adjerid (2021), *Exploit Prediction Scoring System*, Digital Threats Research and Practice, 2(3). See [first.org/epss](https://www.first.org/epss/). |
| [KEV](https://www.cisa.gov/known-exploited-vulnerabilities-catalog) | Public domain (U.S. Gov) | Source: CISA Known Exploited Vulnerabilities Catalog. |
| [Vulnrichment](https://github.com/cisagov/vulnrichment) | CC0 1.0 Universal | Source: CISA Vulnrichment. |
| [GHSA](https://github.com/github/advisory-database) | CC BY 4.0 | Source: GitHub Advisory Database. Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). |
| [Sigma](https://github.com/SigmaHQ/sigma) | Detection Rule License 1.1 | Source: SigmaHQ. Licensed under [DRL 1.1](https://github.com/SigmaHQ/sigma/blob/master/LICENSE.Detection.Rules.md). Rule author attribution is preserved in triples. |
| [ExploitDB](https://gitlab.com/exploit-database/exploitdb) | GPLv2+ | Source: OffSec ExploitDB. Derived factual metadata (IDs, CVE mappings, dates) extracted under [GPLv2+](https://www.gnu.org/licenses/old-licenses/gpl-2.0.html). |
| [MISP Galaxies](https://github.com/MISP/misp-galaxy) | CC0 1.0 / BSD 2-Clause | Source: MISP Project. Dual-licensed under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) and [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause). |
| [LOLBAS](https://github.com/LOLBAS-Project/LOLBAS) | GPLv3 | Source: LOLBAS Project. Licensed under [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html). |
| [LOLDrivers](https://github.com/magicsword-io/LOLDrivers) | Apache 2.0 | Source: LOLDrivers (magicsword.io). |
| [Atomic Red Team](https://github.com/redcanaryco/atomic-red-team) | MIT License | Source: Red Canary Atomic Red Team. Licensed under [MIT](https://opensource.org/licenses/MIT). |
| [NIST 800-53 Mappings](https://github.com/center-for-threat-informed-defense/mappings-explorer) | Apache 2.0 | © MITRE Engenuity, Center for Threat-Informed Defense. |
| [Nuclei Templates](https://github.com/projectdiscovery/nuclei-templates) | MIT License | Source: ProjectDiscovery Nuclei Templates. Licensed under [MIT](https://opensource.org/licenses/MIT). |
| [EUVD](https://euvd.enisa.europa.eu/) | Public (ENISA) | Source: ENISA European Vulnerability Database. Data published by the European Union Agency for Cybersecurity. |
| [OSV](https://osv.dev/) | CC BY 4.0 | Source: OSV (osv.dev). Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). |

## License

Apache 2.0 — see [Source Licensing & Attribution](#source-licensing--attribution) for individual source terms.