Individual skill
implementing-network-traffic-baselining
Builds network traffic baselines from NetFlow/IPFIX CSV or JSON exports using Python pandas, computing hourly/daily volume distributions, per-host and protocol/port statistics, and top-talker profiles, then flags outliers via z-score and IQR anomaly detection. Use when a SOC analyst needs to establish normal traffic patterns and surface deviations such as data exfiltration spikes, beaconing, or unusual port usage from historical flow data.
skills/implementing-network-traffic-baselining/SKILL.md
Source commit 54a798831d2266a3ca61ce68a7acb80b81160d57
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- Observed behavior
- Local scripts
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 35,926
- Repository last updated
- Aug 31, 2026
- License
- Apache-2.0
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- ai-agents Repository topic
- claude-code Repository topic
- cybersecurity Repository topic
- incident-response Repository topic
- mitre-attack Repository topic
- penetration-testing Repository topic
- red-team Repository topic
- security Repository topic
- cloud-security Repository topic
- malware-analysis Repository topic
- netflow Declared skill tag
- ipfix Declared skill tag
- traffic-analysis Declared skill tag
- baselining Declared skill tag
- anomaly-detection Declared skill tag
- pandas Declared skill tag
- network-monitoring Declared skill tag
- data Derived category
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LICENSEasset · 11,255 bytesSKILL.mdcore · 2,605 bytesreferences/api-reference.mddoc · 1,732 bytesscripts/agent.pyruns · 6,805 bytes
Read excerpt
# 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
