analyzing-ransomware-network-indicators
Identify ransomware network indicators including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange via…
Skill by adriannoes
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/adriannoes-analyzing-ransomware-network-indicators
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: data, agent-building
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/analyzing-ransomware-network-indicators
What's inside
- Skill name
analyzing-ransomware-network-indicators- Description
- 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
- License
- Apache-2.0
- Instructions
- 39 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (4)
- LICENSE
- SKILL.md
- api-reference.md · references/
- agent.py · scripts/
SKILL.md (first lines)
# 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)
From adriannoes/awesome-agentic-ai, Apache-2.0.
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Signals
- Stars
- 72
- Forks
- 19
- Open issues
- 0
- Last commit (this folder)
- Aug 28, 2026 (2 months ago)
- Last release
- Oct 24, 2026 (4 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/adriannoes/awesome-agentic-ai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
analyzing-ransomware-network-indicators skill. Agent Mart. https://agentmart.dev/skills/adriannoes-analyzing-ransomware-network-indicators (last updated 2026-10-24)