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mukul975
efca3ec611
Mapped every skill to NIST CSF 2.0 subcategory IDs (GV/ID/PR/DE/RS/RC functions) based on subdomain and content analysis. Restores 11 skills corrupted during prior rebase, re-enriching with ATLAS, D3FEND, NIST AI RMF, and CSF 2.0 fields. All 754 skills now carry structured mappings for all 5 security frameworks: - MITRE ATT&CK (in tags) - MITRE ATLAS v5.5 (atlas_techniques) - MITRE D3FEND v1.3 (d3fend_techniques) - NIST AI RMF 1.0 (nist_ai_rmf) - NIST CSF 2.0 (nist_csf)
66 lines
2.1 KiB
Markdown
66 lines
2.1 KiB
Markdown
---
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name: analyzing-network-flow-data-with-netflow
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description: Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing
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patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis
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to identify flows with abnormal byte counts, connection durations, and periodic timing patterns.
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domain: cybersecurity
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subdomain: network-security
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tags:
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- analyzing
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- network
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- flow
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- data
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- PR.IR-01
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- DE.CM-01
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- ID.AM-03
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- PR.DS-02
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---
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# Analyzing Network Flow Data with Netflow
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## When to Use
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- When investigating security incidents that require analyzing network flow data with netflow
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Familiarity with network security concepts and tools
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- Access to a test or lab environment for safe execution
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- Python 3.8+ with required dependencies installed
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- Appropriate authorization for any testing activities
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## Instructions
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1. Install dependencies: `pip install netflow`
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2. Collect NetFlow/IPFIX data from routers or use the built-in collector: `python -m netflow.collector -p 9995`
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3. Parse captured flow data using `netflow.parse_packet()`.
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4. Analyze flows for:
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- Port scanning: single source to many destinations on same port
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- Data exfiltration: high byte-count outbound flows to unusual destinations
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- C2 beaconing: periodic connections with consistent intervals
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- Volumetric anomalies: traffic spikes beyond baseline thresholds
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5. Generate a prioritized findings report.
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```bash
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python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json
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```
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## Examples
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### Parse NetFlow v9 Packet
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```python
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import netflow
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data, _ = netflow.parse_packet(raw_bytes, templates={})
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for flow in data.flows:
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print(flow.IPV4_SRC_ADDR, flow.IPV4_DST_ADDR, flow.IN_BYTES)
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```
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