Analyzing Network Flow Data With Netflow
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.
MCP get_skill({ skillId: "analyzing-network-flow-data-with-netflow-99590d6d" })Use this skill with your agent
Create a free account and connect via MCP
# 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.
```bash
python scripts/agent.py --flow-file captured_flows.json --output netflow_report.json
```
## Examples
### Parse NetFlow v9 Packet
```python
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)
```Related Skills
More skills in Security & Compliance
1password
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
1password
Set up and use 1Password CLI for sign-in, desktop integration, and reading or injecting secrets.
Accessibility Lead
Accessibility team lead and orchestrator. Use proactively on EVERY task that involves web UI code, HTML, JSX, CSS, React components, web pages, server-side templates (.leaf, .ejs, .erb, .hbs), or any user-facing web content. This agent coordinates the accessibility specialist team and ensures no accessibility requirement is missed. Runs the final review before any UI code is considered complete. Applies to any web framework, server-side templating framework (Vapor/Leaf, Rails/ERB, Django/Jinja, Express/EJS), or vanilla HTML/CSS/JS. Works alongside other team leads (e.g., swift-lead) in multi-language projects.
Accessibility Regression Detector
Detects accessibility regressions by comparing audit results across commits/branches. Tracks score trends, identifies new issues, and validates previous fixes remain in place.
Accessibility Statement
Generates conformance/accessibility statements following W3C or EU model templates. Takes audit results as input, maps to conformance claims, identifies known limitations, and outputs a deployable HTML page or markdown document.
Accessibility Tool Builder
Expert in building accessibility scanning tools, rule engines, document parsers, report generators, and audit automation. WCAG criterion mapping, severity scoring, CLI/GUI scanner architecture, CI/CD integration.
Explore Other Categories
Skills from other categories with shared topics
Analyzing Malicious PDF With Peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
Analyzing PDF Malware With Pdfid
Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis. Activates for requests involving PDF malware analysis, malicious document analysis, PDF exploit investigation, or suspicious attachment triage.
Building Devsecops Pipeline With GitLab CI
Design and implement a comprehensive DevSecOps pipeline in GitLab CI/CD integrating SAST, DAST, container scanning, dependency scanning, and secret detection.