Performing Network Forensics With Wireshark
Capture and analyze network traffic using Wireshark and tshark to reconstruct network events, extract artifacts, and identify malicious communications.
MCP get_skill({ skillId: "performing-network-forensics-with-wireshark-3ac8f413" })Use this skill with your agent
Create a free account and connect via MCP
# Performing Network Forensics with Wireshark
## When to Use
- 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
## 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
### Step 1: Prepare and Validate the Capture File
```bash
# Install Wireshark and tshark
sudo apt-get install wireshark tshark
# Verify the PCAP file
capinfos /cases/case-2024-001/network/capture.pcap
# Output includes: file type, packet count, capture duration, data size
# Example output:
# File name: capture.pcap
# File type: Wireshark/tcpdump/... - pcap
# Number of packets: 1,245,678
# File size: 856 MB
# Data size: 823 MB
# Capture duration: 3600.123456 seconds
# First packet time: 2024-01-15 14:00:00.000000
# Last packet time: 2024-01-15 15:00:00.123456
# Hash the PCAP for integrity
sha256sum /cases/case-2024-001/network/capture.pcap \
> /cases/case-2024-001/network/pcap_hash.txt
# Get a protocol hierarchy statistics overview
tshark -r /cases/case-2024-001/network/capture.pcap -q -z io,phs
```
### Step 2: Filter and Identify Suspicious Traffic
```bash
# Extract conversation statistics
tshark -r /cases/case-2024-001/network/capture.pcap -q -z conv,tcp
# Find top talkers by bytes transferred
tshark -r /cases/case-2024-001/network/capture.pcap -q -z endpoints,ip \
| sort -t$'\t' -k3 -rn | head -20
# Filter for DNS queries (potential C2 or exfiltration)
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "dns.qr == 0" \
-T fields -e frame.time -e ip.src -e dns.qry.name \
> /cases/case-2024-001/analysis/dns_queries.txt
# Find DNS queries to unusual TLDs or long domain names (DNS tunneling)
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "dns.qr == 0 && dns.qry.name matches \"[a-z0-9]{30,}\"" \
-T fields -e frame.time -e ip.src -e dns.qry.name \
> /cases/case-2024-001/analysis/suspicious_dns.txt
# Filter HTTP traffic
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "http.request" \
-T fields -e frame.time -e ip.src -e ip.dst -e http.request.method \
-e http.host -e http.request.uri -e http.user_agent \
> /cases/case-2024-001/analysis/http_requests.txt
# Find connections to known malicious ports
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "tcp.dstport == 4444 || tcp.dstport == 8080 || tcp.dstport == 1337 || tcp.dstport == 6667" \
-T fields -e frame.time -e ip.src -e ip.dst -e tcp.dstport \
> /cases/case-2024-001/analysis/suspicious_ports.txt
# Detect beaconing patterns (regular interval connections)
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "ip.dst == 185.0.0.1" \
-T fields -e frame.time_epoch \
> /tmp/beacon_times.txt
```
### Step 3: Extract Files and Objects from Traffic
```bash
# Export HTTP objects (files transferred over HTTP)
tshark -r /cases/case-2024-001/network/capture.pcap \
--export-objects http,/cases/case-2024-001/analysis/http_objects/
# Export SMB objects
tshark -r /cases/case-2024-001/network/capture.pcap \
--export-objects smb,/cases/case-2024-001/analysis/smb_objects/
# Export DICOM objects (medical imaging)
tshark -r /cases/case-2024-001/network/capture.pcap \
--export-objects dicom,/cases/case-2024-001/analysis/dicom_objects/
# Export FTP data transfers
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "ftp-data" \
-T fields -e ftp-data.data \
--export-objects ftp-data,/cases/case-2024-001/analysis/ftp_objects/
# Hash all extracted objects
find /cases/case-2024-001/analysis/http_objects/ -type f -exec sha256sum {} \; \
> /cases/case-2024-001/analysis/extracted_file_hashes.txt
# Check extracted file hashes against VirusTotal
while read hash filepath; do
echo "Checking $filepath ($hash)"
curl -s "https://www.virustotal.com/api/v3/files/$hash" \
-H "x-apikey: YOUR_API_KEY" | python3 -c "
import json,sys
data=json.load(sys.stdin)
if 'data' in data:
stats=data['data']['attributes']['last_analysis_stats']
print(f' Malicious: {stats[\"malicious\"]}, Undetected: {stats[\"undetected\"]}')
else:
print(' Not found on VT')
"
done < /cases/case-2024-001/analysis/extracted_file_hashes.txt
```
### Step 4: Reconstruct TCP Streams and Sessions
```bash
# Follow a specific TCP stream (stream index 42)
tshark -r /cases/case-2024-001/network/capture.pcap \
-q -z "follow,tcp,ascii,42" \
> /cases/case-2024-001/analysis/stream_42.txt
# Extract all HTTP request-response pairs for a suspicious host
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "http && ip.addr == 185.0.0.1" \
-T fields -e frame.time -e http.request.method -e http.host \
-e http.request.uri -e http.response.code -e http.content_length \
> /cases/case-2024-001/analysis/suspicious_http.txt
# Extract TLS/SSL certificate information
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "tls.handshake.type == 11" \
-T fields -e ip.dst -e tls.handshake.certificate \
> /cases/case-2024-001/analysis/tls_certs.txt
# Extract TLS SNI (Server Name Indication) values
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "tls.handshake.extensions_server_name" \
-T fields -e frame.time -e ip.src -e ip.dst \
-e tls.handshake.extensions_server_name \
> /cases/case-2024-001/analysis/tls_sni.txt
# Extract credentials from unencrypted protocols
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "ftp.request.command == \"USER\" || ftp.request.command == \"PASS\"" \
-T fields -e frame.time -e ip.src -e ftp.request.command -e ftp.request.arg
tshark -r /cases/case-2024-001/network/capture.pcap \
-Y "http.authorization" \
-T fields -e frame.time -e ip.src -e http.host -e http.authorization
```
### Step 5: Use NetworkMiner for Automated Analysis
```bash
# Install NetworkMiner (Mono required on Linux)
sudo apt-get install mono-complete
wget https://www.netresec.com/?download=NetworkMiner -O NetworkMiner.zip
unzip NetworkMiner.zip -d /opt/NetworkMiner/
# Run NetworkMiner
mono /opt/NetworkMiner/NetworkMiner.exe /cases/case-2024-001/network/capture.pcap
# NetworkMiner automatically extracts:
# - Host inventory (OS fingerprinting, open ports)
# - Files transferred over HTTP, FTP, SMB, TFTP
# - Images from web traffic
# - Credentials (plaintext and NTLM hashes)
# - DNS records
# - Session parameters
# - Anomalies and alerts
```
### Step 6: Generate Network Forensics Report
```bash
# Compile findings
cat << 'EOF' > /cases/case-2024-001/analysis/network_forensics_report.txt
NETWORK FORENSICS ANALYSIS REPORT
===================================
Case: 2024-001
Capture File: capture.pcap (856 MB, 1,245,678 packets)
Capture Period: 2024-01-15 14:00 to 15:00 UTC
Analyst: [Examiner Name]
TRAFFIC OVERVIEW:
Total packets: 1,245,678
Unique source IPs: 45
Unique destination IPs: 234
Protocols: TCP (78%), UDP (18%), ICMP (2%), Other (2%)
C2 COMMUNICATION:
Destination: 185.0.0.1:443
Beaconing interval: ~60 seconds
Total connections: 58
Data transferred: 4.2 MB outbound, 12.3 MB inbound
TLS SNI: update-service.malware-c2.com
EXFILTRATION:
Method: HTTPS POST to 185.0.0.1
Volume: 4.2 MB over 45 minutes
Files: 3 ZIP archives extracted from HTTP objects
DNS TUNNELING:
Suspicious queries to: data.evil-dns.com
Average subdomain length: 45 characters
Query count: 1,234 (normal baseline: 50)
EOF
```
## Key Concepts
| Concept | Description |
|---------|-------------|
| PCAP/PCAPNG | Packet capture file formats storing raw network traffic |
| TCP stream | Complete bidirectional communication between two endpoints |
| Deep packet inspection | Analysis of packet payload content beyond header information |
| Beaconing | Regular-interval callbacks from malware to C2 servers |
| DNS tunneling | Encoding data within DNS queries for covert exfiltration |
| TLS/SNI | Server Name Indication revealing the target hostname in encrypted connections |
| Network flow | Summary of communication between endpoints (IPs, ports, bytes, duration) |
| Protocol hierarchy | Statistical breakdown of protocols present in a capture |
## Tools & Systems
| Tool | Purpose |
|------|---------|
| Wireshark | GUI-based packet analyzer with deep protocol dissection |
| tshark | Command-line version of Wireshark for scripted analysis |
| NetworkMiner | Automated network forensic analysis and file extraction |
| tcpdump | Command-line packet capture utility |
| zeek (Bro) | Network security monitor generating structured connection logs |
| ngrep | Network grep for pattern matching in packet content |
| capinfos | PCAP file statistics and metadata utility |
| mergecap | Merge multiple PCAP files into a single capture |
## Common Scenarios
**Scenario 1: Malware C2 Communication Analysis**
Load PCAP in Wireshark, identify beaconing patterns to external IPs, examine TLS certificates for self-signed or unusual issuers, extract HTTP POST data containing encoded commands, correlate C2 IPs with threat intelligence feeds.
**Scenario 2: Data Exfiltration Detection**
Analyze traffic statistics for unusually large outbound transfers, examine DNS query lengths for DNS tunneling indicators, track FTP and HTTP file uploads to external servers, reconstruct exfiltrated files from packet data.
**Scenario 3: Lateral Movement in Enterprise Network**
Filter for SMB, RDP, WMI, and PSExec traffic between internal hosts, identify credential usage patterns across multiple systems, trace the propagation path of the attacker through the network, correlate with Windows Event Log authentication events.
**Scenario 4: Web Application Attack Reconstruction**
Filter HTTP traffic to the web server, identify SQL injection, XSS, and directory traversal attempts, follow the TCP stream of the successful exploit, extract uploaded webshells or payloads, document the attack chain for the incident report.
## Output Format
```
Network Forensics Summary:
Capture: capture.pcap
Duration: 1 hour (14:00-15:00 UTC, 2024-01-15)
Packets: 1,245,678 | Size: 856 MB
Top Suspicious Connections:
192.168.1.50 -> 185.0.0.1:443 (C2, 58 connections, 4.2MB out)
192.168.1.50 -> 10.0.0.25:445 (SMB lateral movement)
192.168.1.50 -> 10.0.0.30:3389 (RDP lateral movement)
Extracted Artifacts:
Files: 23 (3 malicious per VT)
Credentials: 2 plaintext FTP logins
DNS Queries: 1,234 suspicious (possible tunneling)
TLS Certs: 5 self-signed certificates
IOCs Identified:
IPs: 185.0.0.1, 203.0.113.50
Domains: update-service.malware-c2.com, data.evil-dns.com
Hashes: 3 file hashes flagged as malware
```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.