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analyzing-ransomware-network-indicators — Claude Code skill: hunt

Parses Zeek conn.log and NetFlow to spot periodic C2 callbacks, TOR exit node connections and large outbound transfers.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-ransomware-network-indicators -a claude-code
Open on GitHub

What analyzing-ransomware-network-indicators does

Reach for this skill when ransomware activity is suspected on the network, either before encryption starts or while it runs. It takes Zeek conn.log or NetFlow exports in CSV or JSON and turns them into a structured read: connection interval statistics (mean, standard deviation, coefficient of variation) to surface regular callbacks to C2, a cross-check of destination IPs against a TOR exit node list, flags on connections with unusually high outbound byte ratios, and DNS analysis for DGA-like domains and high-entropy subdomains.

Seven steps sit behind that: parse the logs, detect beaconing, check TOR, find exfiltration, analyze DNS, apply composite risk scoring, then generate a report with a timeline and MITRE ATT&CK mapping. You need Python 3.8 or newer with the standard library, plus a TOR exit node list you refresh yourself from the Tor Project or threat intel feeds. A known ransomware C2 IOC list is optional. An outdated TOR list makes the cross-check step noisy, so rebuild it before you work a live incident.

Output is a JSON report: beaconing detections with interval statistics, TOR alerts, exfiltration flow analysis and a composite risk score mapped to techniques. That beats guessing from a single suspicious IP.

What it helps with

  • find periodic C2 callbacks in connection logs
  • cross-check destination IPs against TOR exit nodes
  • catch large outbound flows before encryption
  • scan DNS queries for DGA patterns
  • produce an incident report mapped to MITRE ATT&CK

Example prompts for the agent

  • Parse yesterday's conn.log and show me connections that look like beaconing
  • Cross-check external IPs from NetFlow against the TOR exit node list
  • We suspect pre-encryption exfiltration — look at outbound flows and DNS

How to install analyzing-ransomware-network-indicators

1Skills CLI (Vercel)

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-ransomware-network-indicators -a claude-code

2Manually: put the files in place

~/.claude/skills/analyzing-ransomware-network-indicators/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/analyzing-ransomware-network-indicators/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about analyzing-ransomware-network-indicators

How do I install analyzing-ransomware-network-indicators in Claude?

Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-ransomware-network-indicators -a claude-code. The skill lands in ~/.claude/skills/analyzing-ransomware-network-indicators/SKILL.md.

What does analyzing-ransomware-network-indicators do?

It analyzes Zeek conn.log and NetFlow data to find periodic C2 beaconing, TOR exit node connections, large outbound transfers and suspicious DNS. The result is a JSON report with a risk score and MITRE ATT&CK mapping.

What do I need to run this skill?

Zeek conn.log files or NetFlow exports in CSV or JSON, Python 3.8+ with the standard library, and a current TOR exit node list. A known ransomware C2 IOC list is optional.

Source text

SKILL.md · Apache-2.0
---
name: analyzing-ransomware-network-indicators
description: Identify ransomware-related network indicators, including C2 beaconing patterns, TOR exit node connections, data exfiltration flows, and encryption key exchange, by analyzing Zeek conn.log and NetFlow data. Use when threat hunting for active ransomware network activity or investigating suspected pre-encryption exfiltration during incident response.
domain: cybersecurity
subdomain: threat-hunting
tags:
- ransomware
- c2-beaconing
- zeek
- netflow
- tor
- exfiltration
- network-forensics
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- File Metadata Consistency Validation
- Certificate Analysis
- Application Protocol Command Analysis
- Content Format Conversion
- File Content Analysis
nist_csf:
- DE.CM-01
- DE.AE-02
- DE.AE-07
- ID.RA-05
mitre_attack:
- T1071.001
- T1573
- T1048
- T1567.002
- T1486
mitre_f3:
  version: '1.1'
  tactics:
  - positioning
  - monetization
  techniques:
  - id: T1219
    name: Remote Access Tools
    tactic: positioning
    source: attack
  - id: F1018
    name: Convert to Cryptocurrency
    tactic: monetization
    source: f3
  - id: F1047
    name: Transfer of funds
    tactic: monetization
    source: f3
---

# 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)

© mahipal · Apache-2.0 · mukul975/Anthropic-Cybersecurity-Skills

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