Performing Network Forensics With Wireshark

---
name: performing-network-forensics-with-wireshark
description: Use when capture and analyze network traffic using Wireshark and tshark
  to reconstruct network events, extract artifacts, and identify malicious communications.
  Use when working with performing network forensics with wireshark.
domain: cybersecurity
tags:
- forensics
- network-forensics
- wireshark
- pcap
- packet-analysis
- traffic-analysis
subdomain: digital-forensics
version: '1.0'
author: oyi77
license: Apache-2.0
nist_csf:
- RS.AN-01
- RS.AN-03
- DE.AE-02
- RS.MA-01
category: cybersecurity
---

# Performing Network Forensics With Wireshark

## Overview

Cybersecurity skill for performing network forensics with wireshark. Follows industry best practices and security standards.

## When to Use
**Trigger phrases:**
- "performing network forensics with wireshark"
- "Capture and analyze network traffic using Wireshark and tshark to reconstruct ne"

- When analyzing captured network traffic (PCAP files) from a security incident
- For identifying command-and-control (C2) communications in captured traffic
- When reconstructing data exfiltration activities from packet captures
- During malware analysis to identify network indicators of compromise
- For extracting files, credentials, and artifacts transferred over the network


## 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
- Wireshark or tshark installed for packet analysis
- PCAP/PCAPNG files from network captures (tcpdump, Wireshark, network TAP)
- NetworkMiner for automated artifact extraction
- Sufficient RAM for large capture files (1GB+ PCAPs need 8GB+ RAM)
- Understanding of TCP/IP, HTTP, DNS, TLS protocols
- GeoIP databases for IP geolocation

## Workflow

```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. **Plan Operations** — Define objectives, scope, and success criteria for network forensics operations.
2. **Prepare Environment** — Set up tools, access, and data sources required for network forensics.
3. **Execute Core Workflow** — Use wireshark to perform network forensics operations following established procedures.
4. **Validate Results** — Verify that results meet quality standards and objectives.
5. **Report Findings** — Document results, observations, and recommendations.
6. **Follow Up** — Track remediation actions and verify fixes where applicable.

## Tools

- **wireshark** — Primary tool for this skill
- **Analysis Platform** — Data processing and visualization
- **Collaboration Tools** — Team coordination and knowledge sharing


## Process

1. **Reconnaissance** — Gather target information, identify attack surface, enumerate services
1. **Analysis/Exploitation** — Execute the technique, analyze results, document findings
1. **Reporting** — Document IOCs, write findings, provide remediation recommendations

## Verification

- [ ] All network forensics procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization

## 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. |