mirror of
https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
synced 2026-07-15 12:15:16 +03:00
mukul975
cb8d79e068
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct techniques), verified against MITRE ATT&CK v19.1 via the official mitreattack-python library: 0 revoked, deprecated, or invalid IDs - Curate precise per-skill technique IDs for forensics, malware-analysis, threat-intel, and red-team skills (e.g. DCSync -> T1003.006, Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003) - Reconcile v19.1 tactic restructuring: Defense Evasion split into Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.* family and T1070.001/.002 remapped to active equivalents (T1685.*) - Normalize word-split tags across 35 skills (remove filename-derived stopword tags, add semantic cybersecurity tags) - Add api-reference.md for 3 skills that were missing it - Update README ATT&CK section with accurate v19.1 tactic distribution
64 lines
2.3 KiB
Markdown
64 lines
2.3 KiB
Markdown
---
|
|
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
|
|
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.
|