mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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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
2.6 KiB
2.6 KiB
| name | description | domain | subdomain | tags | version | author | license | d3fend_techniques | nist_csf | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| analyzing-ransomware-network-indicators | Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange via Zeek conn.log and NetFlow analysis | cybersecurity | threat-hunting |
|
1.0 | mahipal | Apache-2.0 |
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Analyzing Ransomware Network Indicators
Overview
Before and during ransomware execution, adversaries establish C2 channels, exfiltrate data, and download encryption keys. This skill analyzes Zeek conn.log and NetFlow data to detect beaconing patterns (regular-interval callbacks), connections to known TOR exit nodes, large outbound data transfers, and suspicious DNS activity associated with ransomware families.
When to Use
- When investigating security incidents that require analyzing ransomware network indicators
- 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
- Zeek conn.log files or NetFlow CSV/JSON exports
- Python 3.8+ with standard library
- TOR exit node list (fetched from Tor Project or threat intel feeds)
- Optional: Known ransomware C2 IOC list
Steps
- Parse Connection Logs — Ingest Zeek conn.log (TSV) or NetFlow records into structured format
- Detect Beaconing Patterns — Calculate connection interval statistics (mean, stddev, coefficient of variation) to identify periodic callbacks
- Check TOR Exit Node Connections — Cross-reference destination IPs against current TOR exit node list
- Identify Data Exfiltration — Flag connections with unusually high outbound byte ratios to external IPs
- Analyze DNS Patterns — Detect DGA-like domain queries and high-entropy subdomains
- Score and Correlate — Apply composite risk scoring across all indicator types
- Generate Report — Produce structured report with timeline and MITRE ATT&CK mapping
Expected Output
- JSON report with beaconing detections and interval statistics
- TOR exit node connection alerts
- Data exfiltration flow analysis
- Composite ransomware risk score with MITRE mapping (T1071, T1573, T1041)