Add 5 new cybersecurity skills: golden ticket detection, traffic baselining, sandbox evasion analysis, domain fronting hunting, SpiderFoot OSINT

2026-07-24 05:30:58 +03:00 · 2026-03-11 00:49:24 +01:00
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+---
+name: implementing-network-traffic-baselining
+description: Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling
+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
+---
+
+# 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.
+
+## 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.