mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
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
74 lines
2.2 KiB
Markdown
74 lines
2.2 KiB
Markdown
---
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name: analyzing-threat-landscape-with-misp
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description: Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics,
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attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute
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IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal
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trends.
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domain: cybersecurity
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subdomain: threat-intelligence
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tags:
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- analyzing
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- threat
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- landscape
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- with
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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d3fend_techniques:
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- File Metadata Consistency Validation
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- Application Protocol Command Analysis
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- Identifier Analysis
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- Content Format Conversion
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- Message Analysis
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nist_csf:
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- ID.RA-01
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- ID.RA-05
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- DE.CM-01
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- DE.AE-02
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---
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# Analyzing Threat Landscape with MISP
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## When to Use
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- When investigating security incidents that require analyzing threat landscape with misp
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Familiarity with threat intelligence concepts and tools
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- Access to a test or lab environment for safe execution
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- Python 3.8+ with required dependencies installed
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- Appropriate authorization for any testing activities
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## Instructions
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1. Install dependencies: `pip install pymisp`
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2. Configure MISP URL and API key.
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3. Run the agent to generate threat landscape analysis:
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- Pull event statistics by threat level and date range
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- Analyze attribute type distributions (IP, domain, hash, URL)
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- Identify top MITRE ATT&CK techniques from event tags
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- Track threat actor activity via galaxy clusters
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- Generate temporal trend analysis of IOC submissions
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```bash
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python scripts/agent.py --misp-url https://misp.local --api-key YOUR_KEY --days 90 --output landscape_report.json
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```
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## Examples
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### Threat Landscape Summary
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```
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Period: Last 90 days
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Events analyzed: 1,247
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Top threat level: High (43%)
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Top attribute type: ip-dst (31%), domain (22%), sha256 (18%)
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Top MITRE technique: T1566 Phishing (89 events)
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Top threat actor: APT28 (34 events)
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```
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