pinkpixel-dev/implementing-network-traffic-baselining
SKILLS/implementing-network-traffic-baselining/SKILL.md
Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling
$ install --global
skillsauthnpx skillsauth add pinkpixel-dev/skills-collection-2 implementing-network-traffic-baseliningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
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3
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9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
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OWASP Dep-Check
50%
Last scanned: May 29, 2026, 4:23 AM125.0s3 files scanned
SKILL.md
- name:
- implementing-network-traffic-baselining
- description:
- Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling
- domain:
- cybersecurity
- subdomain:
- network-security
- version:
- 1.0
- author:
- mahipal
- license:
- 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.
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