Agent-Skills.md

Agent Skills: 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

UncategorizedID: plurigrid/asi/implementing-network-traffic-baselining

Repository

plurigrid/asi
plurigridLicense: MIT
165

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pnpm dlx add-skill https://github.com/plurigrid/asi/tree/HEAD/plugins/asi/skills/implementing-network-traffic-baselining

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plugins/asi/skills/implementing-network-traffic-baselining/SKILL.md

Skill Metadata

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

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

  1. Ingest NetFlow/IPFIX records from CSV or JSON exports
  2. Compute hourly and daily traffic volume distributions (bytes, packets, flows)
  3. Build per-source-IP baseline profiles with mean, median, standard deviation
  4. Calculate protocol and port distribution baselines
  5. Apply z-score anomaly detection to identify statistical outliers
  6. Flag flows exceeding IQR-based thresholds as potential anomalies
  7. 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.