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
68 lines
2.6 KiB
Markdown
68 lines
2.6 KiB
Markdown
---
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name: analyzing-ransomware-network-indicators
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description: Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration
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flows, and encryption key exchange via Zeek conn.log and NetFlow analysis
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domain: cybersecurity
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subdomain: threat-hunting
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tags:
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- ransomware
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- c2-beaconing
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- zeek
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- netflow
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- tor
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- exfiltration
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- network-forensics
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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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- Certificate Analysis
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- Application Protocol Command Analysis
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- Content Format Conversion
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- File Content Analysis
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nist_csf:
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- DE.CM-01
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- DE.AE-02
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- DE.AE-07
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- ID.RA-05
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---
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# Analyzing Ransomware Network Indicators
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## Overview
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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.
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## When to Use
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- When investigating security incidents that require analyzing ransomware network indicators
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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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- Zeek conn.log files or NetFlow CSV/JSON exports
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- Python 3.8+ with standard library
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- TOR exit node list (fetched from Tor Project or threat intel feeds)
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- Optional: Known ransomware C2 IOC list
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## Steps
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1. **Parse Connection Logs** — Ingest Zeek conn.log (TSV) or NetFlow records into structured format
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2. **Detect Beaconing Patterns** — Calculate connection interval statistics (mean, stddev, coefficient of variation) to identify periodic callbacks
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3. **Check TOR Exit Node Connections** — Cross-reference destination IPs against current TOR exit node list
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4. **Identify Data Exfiltration** — Flag connections with unusually high outbound byte ratios to external IPs
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5. **Analyze DNS Patterns** — Detect DGA-like domain queries and high-entropy subdomains
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6. **Score and Correlate** — Apply composite risk scoring across all indicator types
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7. **Generate Report** — Produce structured report with timeline and MITRE ATT&CK mapping
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## Expected Output
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- JSON report with beaconing detections and interval statistics
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- TOR exit node connection alerts
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- Data exfiltration flow analysis
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- Composite ransomware risk score with MITRE mapping (T1071, T1573, T1041)
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