Analyzing Network Packets With Scapy

---
name: analyzing-network-packets-with-scapy
description: Use when craft, send, sniff, and dissect network packets using Scapy
  for protocol analysis, network reconnaissance, and traffic anomaly detection in
  authorized security testing. Use when working with analyzing network packets with
  scapy.
domain: cybersecurity
subdomain: network-security
tags:
- scapy
- packet-analysis
- network-forensics
- protocol-dissection
- pcap
- traffic-analysis
version: '1.0'
author: oyi77
license: Apache-2.0
nist_csf:
- PR.IR-01
- DE.CM-01
- ID.AM-03
- PR.DS-02
category: cybersecurity
---


# Analyzing Network Packets with Scapy

## Overview

Scapy is a Python packet manipulation library that enables crafting, sending, sniffing, and dissecting network packets at granular protocol layers. This skill covers using Scapy for security-relevant tasks including TCP/UDP/ICMP packet crafting, pcap file analysis, protocol field extraction, SYN scan implementation, DNS query analysis, and detecting anomalous traffic patterns such as unusually fragmented packets or malformed headers.


## When to Use
**Trigger phrases:**
- "analyzing network packets with scapy"
- "Craft, send, sniff, and dissect network packets using Scapy for protocol analysi"


- When investigating security incidents that require analyzing network packets with scapy
- 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

- Python 3.8+ with `scapy` library installed (`pip install scapy`)
- Root/administrator privileges for raw socket operations (sniffing, sending)
- Npcap (Windows) or libpcap (Linux) for packet capture
- Authorization to perform packet operations on target network

## Steps

```python
# 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. Read and parse pcap/pcapng files with `rdpcap()` for offline analysis
2. Extract protocol layers (IP, TCP, UDP, DNS, HTTP) and field values
3. Compute traffic statistics: top talkers, protocol distribution, port frequency
4. Detect SYN flood patterns by analyzing TCP flag ratios
5. Identify DNS exfiltration indicators via query length and entropy analysis
6. Craft custom probe packets for authorized network testing
7. Export findings as structured JSON report

## Expected Output

JSON report containing packet statistics, protocol distribution, top source/destination IPs, detected anomalies (SYN floods, DNS tunneling indicators, fragmentation attacks), and per-flow summaries.
## When NOT to Use

- You need to perform the attack, not analyze it (use performing-* skills)
- Task is about detection, not analysis (use detecting-* skills)
- You need to implement controls (use implementing-* skills)
- Task is about threat hunting, not post-incident analysis (use hunting-* skills)
- You don't have access to the artifacts/logs to analyze
- Task requires real-time monitoring (use SOC tools)


## Red Flags

- Performing actions without explicit written authorization from the asset owner
- Testing against production systems without a defined scope and rules of engagement
- Capturing traffic on networks without authorization or privacy considerations
- Leaving packet captures containing sensitive data unencrypted on disk
- Deploying inline blocking rules without testing for false positives first

## Process

1. **Scope** — Define research questions, identify data sources, set time boundaries
1. **Gather** — Collect data from primary sources, APIs, and public records
1. **Synthesize** — Analyze findings, identify patterns, produce actionable report

## Verification

- All steps executed successfully against a test environment before production use
- Output documented with screenshots or logs demonstrating expected behavior
- Captures verified as complete with no dropped packets
- Detection rules tested against known-benign traffic for false positive rate
- Alert thresholds validated and tuned to reduce noise

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |