thedixitjain 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
- Ingest NetFlow/IPFIX records from CSV or JSON exports
- Compute hourly and daily traffic volume distributions (bytes, packets, flows)
- Build per-source-IP baseline profiles with mean, median, standard deviation
- Calculate protocol and port distribution baselines
- Apply z-score anomaly detection to identify statistical outliers
- Flag flows exceeding IQR-based thresholds as potential anomalies
- 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.
Source: mukul975/Anthropic-Cybersecurity-Skills → skills/implementing-network-traffic-baselining/SKILL.md