Implementing Network Traffic Baselining

---
name: implementing-network-traffic-baselining
description: Builds network traffic baselines from NetFlow/IPFIX CSV or JSON exports using Python
  pandas, computing hourly/daily volume distributions, per-host and protocol/port
  statistics, and top-talker profiles, then flags outliers via z-score and IQR anomaly
  detection. Use when a SOC analyst needs to establish normal traffic patterns and
  surface deviations such as data exfiltration spikes, beaconing, or unusual port
  usage from historical flow data.
domain: cybersecurity
subdomain: network-security
tags:
- netflow
- ipfix
- traffic-analysis
- baselining
- anomaly-detection
- pandas
- network-monitoring
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- PR.IR-01
- DE.CM-01
- ID.AM-03
- PR.DS-02
mitre_attack:
- T1046
- T1040
- T1557
- T1071
---

# 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.