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analyzing-network-flow-data-with-netflow skill

by mukul975·mukul975/Anthropic-Cybersecurity-Skills·34k stars·Apache-2.0

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port

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Install the analyzing-network-flow-data-with-netflow skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p ~/.claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/analyzing-network-flow-data-with-netflow ~/.claude/skills/analyzing-network-flow-data-with-netflow
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Analyzing Network Flow Data with Netflow

When to Use

  • When investigating security incidents that require analyzing network flow data with netflow
  • 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

  • Familiarity with network security concepts and tools
  • Access to a test or lab environment for safe execution
  • Python 3.8+ with required dependencies installed
  • Appropriate authorization for any testing activities

Instructions

  1. Install dependencies: pip install netflow
  2. Collect NetFlow/IPFIX data from routers or use the built-in collector: python -m netflow.collector -p 9995
  3. Parse captured flow data using netflow.parse_packet().
  4. Analyze flows for:
  • Port scanning: single source to many destinations on same port
  • Data exfiltration: high byte-count outbound flows to unusual destinations
  • C2 beaconing: periodic connections with consistent intervals
  • Volumetric anomalies: traffic spikes beyond baseline thresholds
  1. Generate a prioritized findings report.
python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json

Examples

Parse NetFlow v9 Packet

import netflow
data, _ = netflow.parse_packet(raw_bytes, templates={})
for flow in data.flows:
    print(flow.IPV4_SRC_ADDR, flow.IPV4_DST_ADDR, flow.IN_BYTES)

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