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Mahipal
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Each rewritten description now states both what the skill does (concrete capability, named tools/artifacts) and an explicit when-to-use trigger, improving agent discovery/activation. Grounded in each skill's own body; changes confined to the `description` field only (bodies and all other frontmatter untouched). Produced by a gated audit->rewrite->recheck loop (548 -> 0 flagged) with a sampled anti-invention check (0 ungrounded). Schema: 817/817 pass. Framework-ID gate: 0 defects.
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2.5 KiB
name, description, domain, subdomain, tags, version, author, license, nist_csf, mitre_attack
| name | description | domain | subdomain | tags | version | author | license | nist_csf | mitre_attack | |||||||||||||||
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| implementing-network-traffic-baselining | 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. | cybersecurity | network-security |
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1.0 | mahipal | Apache-2.0 |
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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
- 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.