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Agent skill

Analyzing Network Traffic For Incidents

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and…

Apache-2.0Auto-check passedSecurity

Install Analyzing Network Traffic For Incidents

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-traffic-for-incidents -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-network-traffic-for-incidents --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyzing-network-traffic-for-incidents .claude/skills/analyzing-network-traffic-for-incidents && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
analyzing-network-traffic-for-incidents
GitHub stars
34k
Token cost
~2.6k tokens
SKILL.md length
775 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and…

  • Works in 6 steps: Capture or Acquire Network Traffic → Identify C2 Communications → Analyze Lateral Movement Traffic → …
  • Tasks that involve Red teaming and adversary simulation
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls ssh

What it does

Analyzing Network Traffic For Incidents is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security, covering Red teaming and adversary simulation, Network security and Security operations. It works with Wireshark. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Red teaming and adversary simulation
  • Tasks that involve Network security
  • Tasks that involve Security operations

Example prompts

  • “Use the analyzing-network-traffic-for-incidents skill to analyz network traffic captures and flow data to identify adversary activity during…”
  • “/analyzing-network-traffic-for-incidents”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Capture or Acquire Network Traffic
  2. Identify C2 Communications
  3. Analyze Lateral Movement Traffic
  4. Detect Data Exfiltration
  5. Extract and Correlate IOCs
  6. Document Network Forensic Findings

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • ssh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use ssh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Analyzing Network Traffic For Incidents loads about 2.6k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 121 tokens; SKILL.md has 775 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~121
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 775 words, ~2,628 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-network-traffic-for-incidents/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-network-traffic-for-incidents
description
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.
domain
cybersecurity
subdomain
incident-response
tags
network-forensics, PCAP-analysis, Wireshark, Zeek, traffic-analysis
mitre_attack
T1071, T1095, T1573, T1572
version
1.0.0
author
mahipal
license
Apache-2.0
nist_csf
RS.MA-01, RS.MA-02, RS.AN-03, RC.RP-01

Analyzing Network Traffic for Incidents

When to Use

  • SIEM alerts on anomalous network traffic patterns requiring deeper investigation
  • C2 beaconing is suspected and needs confirmation through packet-level analysis
  • Data exfiltration volume or destination must be quantified from network evidence
  • Lateral movement between systems needs to be traced through network connections
  • An IDS/IPS alert requires packet-level validation to confirm or dismiss

Do not use for host-based forensic analysis (process execution, file system artifacts); use endpoint forensics tools instead.

Prerequisites

  • Full packet capture (PCAP) infrastructure or on-demand capture capability (network tap, SPAN port)
  • Wireshark installed on the analysis workstation with appropriate display filters knowledge
  • Zeek (formerly Bro) deployed for network metadata generation (conn.log, dns.log, http.log, ssl.log)
  • NetFlow/IPFIX collection from network devices for traffic flow analysis
  • Network architecture diagram showing VLAN layout, firewall placement, and monitoring points
  • Threat intelligence feeds for correlating observed network indicators

Workflow

Step 1: Capture or Acquire Network Traffic

Obtain the relevant traffic data for the investigation:

Live Capture (if incident is active):

bash
# Capture on specific interface filtering by host
tcpdump -i eth0 -w capture.pcap host 10.1.5.42

# Capture C2 traffic to specific external IP
tcpdump -i eth0 -w c2_traffic.pcap host 185.220.101.42

# Capture with rotation (1GB files, keep 10)
tcpdump -i eth0 -w capture_%Y%m%d%H%M.pcap -C 1000 -W 10

From Existing Infrastructure:

  • Export PCAP from full packet capture appliance (Arkime/Moloch, ExtraHop, Corelight)
  • Pull Zeek logs from the Zeek cluster for the investigation timeframe
  • Export NetFlow data from network devices for high-level traffic analysis
Step 2: Identify C2 Communications

Detect command-and-control traffic patterns:

Beaconing Detection (Zeek conn.log):

bash
# Extract connections to external IPs with regular intervals
cat conn.log | zeek-cut ts id.orig_h id.resp_h id.resp_p duration orig_bytes resp_bytes \
  | awk '$4 ~ /^185\.220/' | sort -t. -k1,1n -k2,2n

Wireshark Beacon Analysis:

# Filter for traffic to suspected C2 IP
ip.addr == 185.220.101.42

# Filter HTTPS traffic to non-standard ports
tcp.port != 443 && ssl

# Filter DNS queries for suspicious domains
dns.qry.name contains "evil" or dns.qry.name matches "^[a-z0-9]{32}\."

# Filter HTTP POST (common C2 check-in method)
http.request.method == "POST" && ip.dst == 185.220.101.42

Beaconing characteristics to identify:

  • Regular time intervals between connections (e.g., every 60 seconds with 10-15% jitter)
  • Consistent packet sizes in requests and responses
  • HTTPS to external IPs not associated with legitimate CDNs or services
  • DNS queries with high entropy subdomains (DNS tunneling indicator)
Step 3: Analyze Lateral Movement Traffic

Trace adversary movement between internal systems:

Key protocols for lateral movement detection:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SMB (TCP 445):     PsExec, file share access, ransomware propagation
RDP (TCP 3389):    Remote desktop sessions
WinRM (TCP 5985):  PowerShell remoting
WMI (TCP 135):     Remote command execution
SSH (TCP 22):      Linux lateral movement
DCE/RPC (TCP 135): DCOM-based lateral movement

Wireshark Filters for Lateral Movement:

# SMB lateral movement
smb2 && ip.src == 10.1.5.42 && ip.dst != 10.1.5.42

# RDP connections from compromised host
tcp.dstport == 3389 && ip.src == 10.1.5.42

# Kerberos ticket requests (potential pass-the-ticket)
kerberos.msg_type == 12 && ip.src == 10.1.5.42

# NTLM authentication (potential pass-the-hash)
ntlmssp.auth.username && ip.src == 10.1.5.42
Step 4: Detect Data Exfiltration

Identify unauthorized data transfers leaving the network:

# Identify large outbound transfers in Zeek conn.log
cat conn.log | zeek-cut ts id.orig_h id.resp_h id.resp_p orig_bytes \
  | awk '$5 > 100000000' | sort -t$'\t' -k5 -rn

# DNS tunneling detection (high volume of TXT queries)
cat dns.log | zeek-cut query qtype | grep TXT | cut -f1 \
  | rev | cut -d. -f1,2 | rev | sort | uniq -c | sort -rn | head

# Unusual protocol usage (ICMP tunneling, DNS over HTTPS)
cat conn.log | zeek-cut proto id.resp_p orig_bytes | awk '$1 == "icmp" && $3 > 1000'

Wireshark Exfiltration Filters:

# Large HTTP POST uploads
http.request.method == "POST" && tcp.len > 10000

# FTP data transfers
ftp-data && ip.src == 10.0.0.0/8

# DNS with large TXT responses (tunneling)
dns.resp.type == 16 && dns.resp.len > 200
Step 5: Extract and Correlate IOCs

Pull network-based indicators from traffic analysis:

  • External IP addresses contacted by compromised hosts
  • Domains resolved via DNS during the incident timeframe
  • URLs accessed via HTTP/HTTPS (if SSL inspection is in place)
  • TLS certificate details (subject, issuer, serial number, JA3/JA3S hashes)
  • User-Agent strings from HTTP requests
  • File transfers captured in PCAP (extract using Wireshark Export Objects)
Step 6: Document Network Forensic Findings

Compile analysis into a structured report with evidence references:

  • Reference specific PCAP files, frame numbers, and timestamps for each finding
  • Include packet captures of key evidence as screenshots or exported PDFs
  • Map network activity to the incident timeline
  • Correlate network findings with host-based evidence from endpoint forensics
Show full SKILL.md (372 more words)Show less

Key Concepts

TermDefinition
PCAP (Packet Capture)File format storing raw network packets captured from a network interface for offline analysis
BeaconingRegular, periodic network connections from a compromised host to a C2 server, identifiable by consistent timing intervals
JA3/JA3STLS client and server fingerprinting method based on the ClientHello and ServerHello parameters; unique per application
NetFlow/IPFIXNetwork traffic metadata (source, destination, ports, bytes, duration) collected by routers and switches without full packet capture
DNS TunnelingTechnique encoding data in DNS queries and responses to exfiltrate data or maintain C2 through DNS protocol
Network TapHardware device that creates an exact copy of network traffic for monitoring without impacting network performance
Zeek LogsStructured metadata logs generated by the Zeek network analysis framework covering connections, DNS, HTTP, SSL, and more

Tools & Systems

  • Wireshark: Open-source packet analyzer for deep inspection of network protocols at the packet level
  • Zeek (formerly Bro): Network analysis framework generating structured metadata logs from live or captured traffic
  • Arkime (formerly Moloch): Open-source full packet capture and search platform for large-scale network forensics
  • NetworkMiner: Network forensic analysis tool for extracting files, images, and credentials from PCAP files
  • RITA (Real Intelligence Threat Analytics): Open-source beacon detection and DNS tunneling analysis tool for Zeek logs

Common Scenarios

Scenario: Confirming C2 Beaconing and Quantifying Exfiltration

Context: EDR detects a suspicious process on a workstation but cannot determine the volume of data exfiltrated. Network team provides PCAP from the full packet capture appliance covering the incident timeframe.

Approach:

  1. Filter PCAP to traffic from the compromised host IP to external destinations
  2. Identify the C2 channel by analyzing connection timing patterns (beacon detection)
  3. Extract TLS certificate and JA3 hash from the C2 connection for IOC generation
  4. Calculate total bytes transferred to C2 infrastructure over the incident duration
  5. Check for additional exfiltration channels (DNS tunneling, cloud storage uploads)
  6. Extract any unencrypted files transferred using Wireshark Export Objects feature

Pitfalls:

  • Analyzing only HTTP traffic when C2 is operating over HTTPS without SSL inspection
  • Missing DNS tunneling because the data volume per query is small (but total over time is significant)
  • Not correlating network timestamps with endpoint timestamps (timezone mismatches)
  • Overlooking legitimate cloud services abused for exfiltration (OneDrive, Google Drive, Dropbox)

Output Format

NETWORK TRAFFIC ANALYSIS REPORT
=================================
Incident:         INC-2025-1547
Analyst:          [Name]
Capture Source:   Arkime full packet capture
Analysis Period:  2025-11-15 14:00 UTC - 2025-11-15 18:00 UTC
Total PCAP Size:  4.7 GB

C2 COMMUNICATIONS
Source:           10.1.5.42 (WKSTN-042)
Destination:      185.220.101.42:443 (HTTPS)
Beacon Interval:  60 seconds ± 12% jitter
Sessions:         237 connections over 4 hours
JA3 Hash:         a0e9f5d64349fb13191bc781f81f42e1
TLS Certificate:  CN=update.evil[.]com (self-signed)
Total Data Sent:  147 MB (outbound)
Total Data Recv:  2.3 MB (inbound - commands)

LATERAL MOVEMENT
10.1.5.42 → 10.1.10.15 (SMB, TCP 445) - 14:35 UTC
10.1.5.42 → 10.1.10.20 (RDP, TCP 3389) - 14:42 UTC
10.1.5.42 → 10.1.1.5  (LDAP, TCP 389) - 15:10 UTC

EXFILTRATION SUMMARY
Protocol:         HTTPS to C2 server
Volume:           147 MB outbound
Duration:         14:23 UTC - 18:00 UTC
Files Extracted:  [list if recoverable from unencrypted channels]

DNS ANALYSIS
Suspicious Queries: 0 DNS tunneling indicators
DGA Detection:      0 algorithmically generated domains

EVIDENCE REFERENCES
PCAP File:        INC-2025-1547_capture.pcap (SHA-256: ...)
Zeek Logs:        /logs/zeek/2025-11-15/ (conn.log, ssl.log, dns.log)

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/analyzing-network-traffic-for-incidents of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

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TShark Traffic AnalysisAgentSecOps/SecOpsAgentKit2201 repos~4.8kAutomated safety check: NotesCustom licence
Cybersecurityohmyjahh/xquads-squads277—~895Automated safety check: PassMIT
Dfirtransilienceai/communitytools563—~1.5kAutomated safety check: PassMIT
Protocol Reverse Engineeringwshobson/agents40k8 repos~3.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Analyzing Network Traffic For Incidents

What does Analyzing Network Traffic For Incidents do?

Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and…. Analyzing Network Traffic For Incidents is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts.

When should I use Analyzing Network Traffic For Incidents?

Analyzing Network Traffic For Incidents fits situations like: tasks that involve Red teaming and adversary simulation; tasks that involve Network security; tasks that involve Security operations.

How do I install Analyzing Network Traffic For Incidents in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-traffic-for-incidents -a claude-code`. Or copy the skill folder (skills/analyzing-network-traffic-for-incidents in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-network-traffic-for-incidents in your project. Claude Code loads it when a task matches its description.

How do I install Analyzing Network Traffic For Incidents in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-traffic-for-incidents -a codex`. Or copy the skill folder (skills/analyzing-network-traffic-for-incidents in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-network-traffic-for-incidents in your project. Codex loads it when a task matches its description.

Can I use Analyzing Network Traffic For Incidents in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-traffic-for-incidents -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-network-traffic-for-incidents, .gemini/skills/analyzing-network-traffic-for-incidents, .github/skills/analyzing-network-traffic-for-incidents and .opencode/skills/analyzing-network-traffic-for-incidents in your project.

What does Analyzing Network Traffic For Incidents need to run?

Going by SKILL.md and its folder, Analyzing Network Traffic For Incidents needs Python for the scripts in its folder and the command-line tools its instructions call (ssh). Our summary lists: Python 3.

Does Analyzing Network Traffic For Incidents access the network?

SKILL.md contains no URLs. Its commands use ssh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Analyzing Network Traffic For Incidents safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Analyzing Network Traffic For Incidents use?

Analyzing Network Traffic For Incidents is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analyzing Network Traffic For Incidents use?

About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 707 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Network Traffic For Incidents?

Skills that share tags, products or a category with Analyzing Network Traffic For Incidents: Incident Response Network (LeoYeAI/openclaw-master-skills, 2.2k stars), TShark Traffic Analysis (AgentSecOps/SecOpsAgentKit, 220 stars), Cybersecurity (ohmyjahh/xquads-squads, 277 stars) and Dfir (transilienceai/communitytools, 563 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Network Traffic For Incidents?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.