security analyst security skill risk: medium
Ransomware Network Indicators Analyzer
This prompt instructs the model to parse Zeek conn.log or NetFlow data, detect beaconing patterns, TOR exit node connections, data exfiltration, and suspicious DNS activity, then p…
- Policy sensitive
- Human review
SKILL 4 files · 2 folders
SKILL.md
--- name: analyzing-ransomware-network-indicators description: "Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration" --- # 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 1. **Parse Connection Logs** — Ingest Zeek conn.log (TSV) or NetFlow records into structured format 2. **Detect Beaconing Patterns** — Calculate connection interval statistics (mean, stddev, coefficient of variation) to identify periodic callbacks 3. **Check TOR Exit Node Connections** — Cross-reference destination IPs against current TOR exit node list 4. **Identify Data Exfiltration** — Flag connections with unusually high outbound byte ratios to external IPs 5. **Analyze DNS Patterns** — Detect DGA-like domain queries and high-entropy subdomains 6. **Score and Correlate** — Apply composite risk scoring across all indicator types 7. **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)
REQUIRED CONTEXT
- Zeek conn.log files or NetFlow CSV/JSON exports
- TOR exit node list
OPTIONAL CONTEXT
- Known ransomware C2 IOC list
EXPECTED OUTPUT
- Format
- structured_report
- Schema
- json_schema · beaconing detections and interval statistics, TOR exit node connection alerts, Data exfiltration flow analysis, Composite ransomware risk score with MITRE mapping
- Constraints
-
- 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)
SUCCESS CRITERIA
- Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration
- Produce structured report with timeline and MITRE ATT&CK mapping
CAVEATS
- Dependencies
-
- 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
QUALITY
- OVERALL
- 0.85
- CLARITY
- 0.90
- SPECIFICITY
- 0.85
- REUSABILITY
- 0.80
- COMPLETENESS
- 0.85
USAGE
Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.
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