# Analyzing Network Flow Data With Netflow

> Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns using the Python netflow library.

- Skill: `mukul975/analyzing-network-flow-data-with-netflow` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add mukul975/analyzing-network-flow-data-with-netflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mukul975/analyzing-network-flow-data-with-netflow/raw
- Safety review: CAUTION (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security, Coding & Dev Tools, Data & Analytics, Data Analysis, Vulnerability Scanning
- Tags: Anomaly Detection, C2 Beaconing, Data Exfiltration, Ipfix, Netflow, Network Flow, Port Scanning, Python
- License: Apache-2.0
- Author: mukul975 (https://skillmd.com/u/mukul975)
- Updated: 2026-07-06
- Page: https://skillmd.com/skills/mukul975/analyzing-network-flow-data-with-netflow

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# 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
5. Generate a prioritized findings report.

```bash
python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json
```

## Examples

### Parse NetFlow v9 Packet
```python
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)
```