analyzing-network-flow-data-with-netflow

name	analyzing-network-flow-data-with-netflow
description	Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing patterns. Use when working with analyzing network flow data with netflow.
domain	cybersecurity
tags	["analyzing","network","flow","data"]
subdomain	network-security
version	1.0
author	oyi77
license	Apache-2.0
nist_csf	["PR.IR-01","DE.CM-01","ID.AM-03","PR.DS-02"]

Analyzing Network Flow Data With Netflow

Overview

Cybersecurity skill for analyzing network flow data with netflow. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "analyzing network flow data with netflow"

• "Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning"

• When investigating security incidents that require analyzing network flow data with netflow

• When building detection rules or threat hunting queries for this domain

• When SOC analysts need structured procedures for this analysis type

• When validating security monitoring coverage for related attack techniques

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

Prerequisites

• Familiarity with network security concepts and tools
• Access to a test or lab environment for safe execution
• Python 3.8+ with required dependencies installed
• Appropriate authorization for any testing activities

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}

1. Scope the Analysis — Define what network flow data artifacts or data sources to examine and the investigation timeline.
2. Preserve Evidence — Create forensic copies of relevant data. Maintain chain of custody documentation.