analyzing-network-flow-data-with-netflow
Makes your agent understand network traffic data to find unusual activities. It identifies patterns that could indicate attacks or data theft.
What it does
This tool analyzes network traffic data (called NetFlow/IPFIX) to find suspicious activities. It looks for unusual traffic patterns that might indicate attacks or data leaks.
When to use it
- ✓To investigate a network security incident.
- ✓To hunt for malicious network activities.
- ✓To check if your network is protected against certain attack types.
Example of what to ask your AI
“Analyze network traffic data to find suspicious activities.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool reads network traffic data, compares it to normal traffic, and alerts about unusual activities like data spikes, strange connections, or repetitive patterns that could be dangerous.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-flow-data-with-netflowOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/analyzing-network-flow-data-with-netflow/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
- Install dependencies:
pip install netflow - Collect NetFlow/IPFIX data from routers or use the built-in collector:
python -m netflow.collector -p 9995 - Parse captured flow data using
netflow.parse_packet(). - 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
- 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)
FAQ
What are NetFlow/IPFIX?
They are formats that describe network traffic, like who connected to whom, for how long, and how much data was exchanged.
What kinds of suspicious activities can it find?
It can detect port scanning (attempts to find open ports), data exfiltration (information theft), communication with malicious servers (C2 beaconing), and abnormal traffic spikes.
Do I need to be a network expert to use it?
Some basic network security knowledge is helpful, but the tool is designed to assist with analysis even if you're not an expert.
Details
Original description by the author
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.
Popularity
- GitHub stars
- 35k
- Ranking position
- #5472
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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