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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

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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.