Implementing Network Traffic Baselining

---
name: implementing-network-traffic-baselining
description: Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly Tespit, and hourly/daily traffic pattern profiling
tags:
- traffic-analysis
- pandas
- netflow
- anomaly-Tespit
- baselining
- network-security
- fetih
- ipfix
- cybersecurity
- network-monitoring
- siber-güvenlik
triggers:
- IDS
- IPS
- alert
- ağ güvenliği
- baselining
- firewall
- implementing
- network
- network security
- traffic
category: network-security
source_subdomain: network-security
nist_csf:
- PR.IR-01
- DE.CM-01
- ID.AM-03
- PR.DS-02
adapted_for: fetih
---

# Implementing Network Traffic Baselining


## Genel Bakış

Network traffic baselining establishes normal communication patterns by analyzing historical NetFlow/IPFIX data to create statistical profiles of expected behavior. bu 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.


## Ne Zaman Kullanılır

- Dağıt:ing yaparken or configuring implementing network traffic baselining capabilities in your environment
- establishing yaparken: security controls aligned to compliance requirements
- building yaparken or improving security architecture for this domain
- conducting yaparken security assessments that require this implementation

## Ön Gereksinimler

- 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)

## Adımlar

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 Tespit 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.

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