The Network Traffic Baselining skill provides a systematic approach to establishing normal communication patterns by analyzing historical NetFlow or IPFIX data. Utilizing Python's pandas library, this skill processes CSV or JSON exports to compute traffic distributions over hourly and daily intervals. It generates detailed statistics for each host, including byte and packet counts, as well as protocol and port usage ratios. This information is crucial for security operations center (SOC) analysts who need to understand baseline network behavior and identify any deviations that may indicate security incidents.

The skill employs statistical methods, specifically z-score and interquartile range (IQR) anomaly detection techniques, to flag unusual traffic patterns. By comparing current traffic data against established baselines, SOC analysts can quickly identify potential threats such as data exfiltration spikes, beaconing activities, or abnormal port usage. This capability is essential for maintaining network security and compliance with organizational policies.

To effectively use this skill, users must have access to historical flow data, ideally covering at least seven days, to create reliable baselines. The skill's output includes a JSON report that details traffic baselines, per-host statistics, and any detected anomalies, providing a comprehensive overview of network behavior. This tool is particularly beneficial for security assessments and when configuring security controls aligned with compliance requirements.

In summary, the Network Traffic Baselining skill is designed for SOC analysts and security professionals who need to establish a clear understanding of normal network traffic patterns and quickly identify deviations that could indicate security threats. Its reliance on established statistical methods ensures that the analysis is both accurate and actionable.