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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)
2.2 KiB
2.2 KiB
name, description, domain, subdomain, tags, version, author, license, nist_csf
| name | description | domain | subdomain | tags | version | author | license | nist_csf | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| implementing-network-traffic-baselining | Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling | cybersecurity | network-security |
|
1.0 | mahipal | Apache-2.0 |
|
Implementing Network Traffic Baselining
Overview
Network traffic baselining establishes normal communication patterns by analyzing historical NetFlow/IPFIX data to create statistical profiles of expected behavior. This skill uses Python pandas to compute hourly and daily traffic distributions, per-host byte/packet counts, protocol ratios, and top-N talker profiles. Anomalies are detected using z-score thresholds and IQR (interquartile range) outlier methods, enabling SOC analysts to identify deviations such as data exfiltration spikes, beaconing patterns, and unusual port usage.
When to Use
- When deploying or configuring implementing network traffic baselining capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- NetFlow v5/v9 or IPFIX flow data exported as CSV or JSON
- Python 3.8+ with pandas and numpy libraries
- Historical flow data (minimum 7 days recommended for baseline)
Steps
- Ingest NetFlow/IPFIX records from CSV or JSON exports
- Compute hourly and daily traffic volume distributions (bytes, packets, flows)
- Build per-source-IP baseline profiles with mean, median, standard deviation
- Calculate protocol and port distribution baselines
- Apply z-score anomaly detection to identify statistical outliers
- Flag flows exceeding IQR-based thresholds as potential anomalies
- Generate baseline report with anomaly alerts
Expected Output
JSON report containing traffic baselines (hourly/daily profiles), per-host statistics, detected anomalies with z-scores, and top talker rankings with deviation indicators.