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sica-fondt/knowledge/cyber-skills/skills/implementing-network-traffic-baselining/SKILL.md
Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure 
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

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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
netflow
ipfix
traffic-analysis
baselining
anomaly-detection
pandas
network-monitoring
1.0 mahipal Apache-2.0
PR.IR-01
DE.CM-01
ID.AM-03
PR.DS-02

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.