performing-network-forensics-with-wireshark

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-network-forensics-with-wireshark
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summary

Capture and analyze network traffic using Wireshark and tshark to reconstruct network events, extract artifacts, and identify malicious communications.

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

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

# 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

# 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

# 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

# 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

# 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

# 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

ConceptDescription
PCAP/PCAPNGPacket capture file formats storing raw network traffic
TCP streamComplete bidirectional communication between two endpoints
Deep packet inspectionAnalysis of packet payload content beyond header information
BeaconingRegular-interval callbacks from malware to C2 servers
DNS tunnelingEncoding data within DNS queries for covert exfiltration
TLS/SNIServer Name Indication revealing the target hostname in encrypted connections
Network flowSummary of communication between endpoints (IPs, ports, bytes, duration)
Protocol hierarchyStatistical breakdown of protocols present in a capture

Tools & Systems

ToolPurpose
WiresharkGUI-based packet analyzer with deep protocol dissection
tsharkCommand-line version of Wireshark for scripted analysis
NetworkMinerAutomated network forensic analysis and file extraction
tcpdumpCommand-line packet capture utility
zeek (Bro)Network security monitor generating structured connection logs
ngrepNetwork grep for pattern matching in packet content
capinfosPCAP file statistics and metadata utility
mergecapMerge 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
how to use performing-network-forensics-with-wireshark

How to use performing-network-forensics-with-wireshark on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add performing-network-forensics-with-wireshark
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/performing-network-forensics-with-wireshark

The skills CLI fetches performing-network-forensics-with-wireshark from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/performing-network-forensics-with-wireshark

Reload or restart Cursor to activate performing-network-forensics-with-wireshark. Access the skill through slash commands (e.g., /performing-network-forensics-with-wireshark) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.652 reviews
  • Nikhil Nasser· Dec 8, 2024

    Solid pick for teams standardizing on skills: performing-network-forensics-with-wireshark is focused, and the summary matches what you get after install.

  • Mei Yang· Dec 8, 2024

    performing-network-forensics-with-wireshark has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Zara Agarwal· Nov 27, 2024

    performing-network-forensics-with-wireshark has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ava Tandon· Nov 27, 2024

    performing-network-forensics-with-wireshark fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • James Iyer· Nov 27, 2024

    Solid pick for teams standardizing on skills: performing-network-forensics-with-wireshark is focused, and the summary matches what you get after install.

  • Li Gill· Nov 3, 2024

    I recommend performing-network-forensics-with-wireshark for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Li Rao· Oct 22, 2024

    performing-network-forensics-with-wireshark reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • James Patel· Oct 18, 2024

    Keeps context tight: performing-network-forensics-with-wireshark is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kofi Tandon· Oct 18, 2024

    Registry listing for performing-network-forensics-with-wireshark matched our evaluation — installs cleanly and behaves as described in the markdown.

  • James Menon· Oct 18, 2024

    We added performing-network-forensics-with-wireshark from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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