Red Analyzing Network Flow Data With Netflow

---
name: red-analyzing-network-flow-data-with-netflow
description: Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns.
domain: red
category: evasion
hide: true
license: Apache-2.0
metadata:
  manifest_entry: src/corpus/red-analyzing-network-flow-data-with-netflow/SKILL.md
---
## Helper prerequisites

This bundled skill supplies operational methodology; execution should use evidence_exec or the documented underlying security binaries directly in accordance with engagement scope.



# 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

## Detection Gaps & Validation

- **Sampling loss blinds you:** sampled NetFlow (e.g., 1:1000 on the router) drops most low-volume flows — beaconing and slow exfil disappear. Check the IPFIX `samplingInterval`/`SAMPLING_INTERVAL` field; on sampled exporters, scale `IN_BYTES`/`IN_PKTS` and never trust raw flow counts for scan detection.
- **Flow records are metadata only:** NetFlow sees the 5-tuple, bytes, and timing — not payload. DNS C2 over DoH/DoT (port 443/853) and TLS-encrypted exfil look like ordinary HTTPS; pivot on destination reputation, JA3 from a sidecar sensor, and per-flow byte asymmetry, not content.
- **Low-and-slow beats fixed thresholds:** attackers pace beacons with jitter and keep flows under volumetric alarms. Compute inter-flow interval variance per (src,dst,dstport) over hours, not a single window; flag near-constant periods even at tiny byte counts.
- **Active/inactive timeouts split sessions:** one long transfer becomes many flow records (default 30s active / 15s inactive timeout), inflating flow counts and hiding total volume. Aggregate by 5-tuple before scoring exfil.
- **Validate the pipeline fires:** replay a known-bad pcap through `nfreplay`/softflowd to your collector (`python -m netflow.collector -p 9995`) and confirm the scan/beacon rule alerts on it; if no alert, the template (v9 template ID mismatch) or field mapping is broken.
- **FP tuning:** baseline backup windows, CDN/cloud sync, and SaaS keepalives per-host before alerting, or legitimate periodic traffic floods the beaconing detector.

## 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
evidence_exec --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)
```