mirror of
https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
synced 2026-08-03 17:30:19 +03:00
Mahipal
2fb6a9faff
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.
67 lines
2.5 KiB
Markdown
67 lines
2.5 KiB
Markdown
---
|
|
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
|
|
---
|
|
|
|
# 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.
|