mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 08:30:20 +00:00
24f816b6a3
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
58 lines
2.2 KiB
Markdown
58 lines
2.2 KiB
Markdown
---
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name: implementing-network-traffic-baselining
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description: Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score
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anomaly detection, and hourly/daily traffic pattern profiling
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domain: cybersecurity
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subdomain: network-security
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tags:
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- netflow
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- ipfix
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- traffic-analysis
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- baselining
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- anomaly-detection
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- pandas
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- network-monitoring
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- PR.IR-01
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- DE.CM-01
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- ID.AM-03
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- PR.DS-02
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---
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# Implementing Network Traffic Baselining
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## Overview
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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.
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## When to Use
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- When deploying or configuring implementing network traffic baselining capabilities in your environment
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- When establishing security controls aligned to compliance requirements
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- When building or improving security architecture for this domain
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- When conducting security assessments that require this implementation
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## Prerequisites
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- NetFlow v5/v9 or IPFIX flow data exported as CSV or JSON
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- Python 3.8+ with pandas and numpy libraries
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- Historical flow data (minimum 7 days recommended for baseline)
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## Steps
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1. Ingest NetFlow/IPFIX records from CSV or JSON exports
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2. Compute hourly and daily traffic volume distributions (bytes, packets, flows)
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3. Build per-source-IP baseline profiles with mean, median, standard deviation
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4. Calculate protocol and port distribution baselines
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5. Apply z-score anomaly detection to identify statistical outliers
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6. Flag flows exceeding IQR-based thresholds as potential anomalies
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7. Generate baseline report with anomaly alerts
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## Expected Output
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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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