mirror of
https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
synced 2026-07-19 22:19:39 +03:00
mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
2.3 KiB
2.3 KiB
name, description, domain, subdomain, tags, version, author, license, d3fend_techniques, nist_csf, mitre_attack
| name | description | domain | subdomain | tags | version | author | license | d3fend_techniques | nist_csf | mitre_attack | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| analyzing-threat-landscape-with-misp | Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends. | cybersecurity | threat-intelligence |
|
1.0 | mahipal | Apache-2.0 |
|
|
|
Analyzing Threat Landscape with MISP
When to Use
- When investigating security incidents that require analyzing threat landscape with misp
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with threat intelligence concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
- Install dependencies:
pip install pymisp - Configure MISP URL and API key.
- Run the agent to generate threat landscape analysis:
- Pull event statistics by threat level and date range
- Analyze attribute type distributions (IP, domain, hash, URL)
- Identify top MITRE ATT&CK techniques from event tags
- Track threat actor activity via galaxy clusters
- Generate temporal trend analysis of IOC submissions
python scripts/agent.py --misp-url https://misp.local --api-key YOUR_KEY --days 90 --output landscape_report.json
Examples
Threat Landscape Summary
Period: Last 90 days
Events analyzed: 1,247
Top threat level: High (43%)
Top attribute type: ip-dst (31%), domain (22%), sha256 (18%)
Top MITRE technique: T1566 Phishing (89 events)
Top threat actor: APT28 (34 events)