What it does. Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-flow-data-with-netflow, or copy the skill folder into ~/.claude/skills/analyzing-network-flow-data-with-netflow/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/analyzing-network-flow-data-with-netflow/SKILL.md

SKILL.md (verbatim)

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.
domain: cybersecurity
subdomain: network-security
tags:
- analyzing
- network
- flow
- data
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:
- T1071
- T1048
- T1046
- T1095

Analyzing Network Flow Data with Netflow

When to Use

• 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

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

Instructions

1. Install dependencies: pip install netflow
2. Collect NetFlow/IPFIX data from routers or use the built-in collector: python -m netflow.collector -p 9995
3. Parse captured flow data using netflow.parse_packet().
4. Analyze flows for:
   • Port scanning: single source to many destinations on same port
   • Data exfiltration: high byte-count outbound flows to unusual destinations
   • C2 beaconing: periodic connections with consistent intervals
   • Volumetric anomalies: traffic spikes beyond baseline thresholds
5. Generate a prioritized findings report.

python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json

Examples

Parse NetFlow v9 Packet

import netflow
data, _ = netflow.parse_packet(raw_bytes, templates={})
for flow in data.flows:
    print(flow.IPV4_SRC_ADDR, flow.IPV4_DST_ADDR, flow.IN_BYTES)

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

API Reference: NetFlow v9/IPFIX Analysis

Python netflow Library

import netflow
# Parse a raw NetFlow packet
packet, templates = netflow.parse_packet(raw_bytes, templates={})
# templates must persist between calls for v9/IPFIX
for flow in packet.flows:
    flow.IPV4_SRC_ADDR  # Source IP
    flow.IPV4_DST_ADDR  # Destination IP
    flow.L4_SRC_PORT    # Source port
    flow.L4_DST_PORT    # Destination port
    flow.PROTOCOL       # IP protocol (6=TCP, 17=UDP)
    flow.IN_BYTES       # Bytes transferred
    flow.IN_PKTS        # Packet count
    flow.TCP_FLAGS      # TCP flags bitmask
    flow.FIRST_SWITCHED # Flow start time
    flow.LAST_SWITCHED  # Flow end time

CLI Tools

python -m netflow.collector -p 9995 -D /tmp/flows  # Collector
python -m netflow.analyzer -f /tmp/flows/*.json     # Analyzer

NetFlow v9 Field Types

Detection Algorithms

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