saint-graves/sica-fondt
1
0
Fork 0
mirror of https://github.com/SHOGGOTH-SECTOR/sica-fondt.git synced 2026-08-01 00:23:15 +00:00
Code Issues Packages Projects Releases Wiki Activity
sica-fondt/knowledge/cyber-skills/skills/analyzing-threat-landscape-with-misp/SKILL.md
Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure 
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

2.2 KiB
Raw Blame History

name description domain subdomain tags version author license d3fend_techniques nist_csf
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
analyzing
threat
landscape
with
1.0 mahipal Apache-2.0
File Metadata Consistency Validation
Application Protocol Command Analysis
Identifier Analysis
Content Format Conversion
Message Analysis
ID.RA-01
ID.RA-05
DE.CM-01
DE.AE-02

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

  1. Install dependencies: pip install pymisp
  2. Configure MISP URL and API key.
  3. 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)