Agent skill

Analyzing Network Traffic Of Malware

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement…

Apache-2.0Auto-check passedSecurity

Install Analyzing Network Traffic Of Malware

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

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-network-traffic-of-malware --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-of-malware .claude/skills/analyzing-network-traffic-of-malware && 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-of-malware
GitHub stars
34k
Token cost
~3k tokens
SKILL.md length
598 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement…

  • Works in 6 steps: Initial PCAP Overview → Analyze DNS Activity → Analyze HTTP/HTTPS C2 Communication → …
  • 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 python3

What it does

Analyzing Network Traffic Of Malware is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding, malware PCAP analysis, or network-based malware detection.

Its SKILL.md is about 3k 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 Incident response. 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 Incident response

Example prompts

  • “Use the analyzing-network-traffic-of-malware skill to analyz network traffic generated by malware during sandbox execution or live incident response…”
  • “/analyzing-network-traffic-of-malware”

Requirements

  • Python 3

Workflow steps

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

  1. Initial PCAP Overview
  2. Analyze DNS Activity
  3. Analyze HTTP/HTTPS C2 Communication
  4. Detect Beaconing Patterns
  5. Generate Network Detection Signatures
  6. Extract Files and Artifacts from Traffic

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:

    • python3

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

  • Network

    No URLs in SKILL.md.

    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 Of Malware loads about 3k tokens when it runs, and up to ~3.7k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 598 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~103
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.7k

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). 598 words, ~2,961 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-network-traffic-of-malware/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-of-malware
description
Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding, malware PCAP analysis, or network-based malware detection.
domain
cybersecurity
subdomain
malware-analysis
tags
malware, network-analysis, PCAP, Wireshark, C2-detection
version
1.0.0
author
mahipal
license
Apache-2.0
nist_csf
DE.AE-02, RS.AN-03, ID.RA-01, DE.CM-01
mitre_attack
T1071.001, T1571, T1573, T1095

Analyzing Network Traffic of Malware

When to Use

  • Sandbox execution has captured a PCAP file and the network behavior needs detailed analysis
  • Identifying the C2 protocol structure for writing network detection signatures
  • Determining what data the malware exfiltrates and to which external infrastructure
  • Analyzing DNS tunneling, domain generation algorithms (DGA), or fast-flux behavior
  • Creating Suricata/Snort signatures based on observed malware network patterns

Do not use for host-based analysis of malware behavior; use Cuckoo sandbox reports or Volatility memory analysis for process-level activity.

Prerequisites

  • Wireshark 4.x installed for interactive PCAP analysis
  • tshark (Wireshark CLI) for scripted packet extraction
  • Zeek installed for automated metadata generation from PCAPs
  • Suricata with ET Open/ET Pro rulesets for signature matching
  • NetworkMiner for file extraction and credential detection from PCAPs
  • Python 3.8+ with scapy and dpkt for programmatic packet analysis

Workflow

Step 1: Initial PCAP Overview

Get a high-level understanding of the network traffic:

bash
# Capture statistics
capinfos malware.pcap

# Protocol hierarchy
tshark -r malware.pcap -q -z io,phs

# Endpoint statistics (top talkers)
tshark -r malware.pcap -q -z endpoints,ip

# Conversation statistics
tshark -r malware.pcap -q -z conv,tcp

# DNS query summary
tshark -r malware.pcap -q -z dns,tree
Step 2: Analyze DNS Activity

Examine DNS queries for DGA, tunneling, or C2 domain resolution:

bash
# Extract all DNS queries
tshark -r malware.pcap -T fields -e frame.time -e dns.qry.name -e dns.a \
  -Y "dns.flags.response == 1" | sort

# Detect DGA patterns (high entropy domain names)
python3 << 'PYEOF'
import math
from collections import Counter

def entropy(s):
    p = [n/len(s) for n in Counter(s).values()]
    return -sum(pi * math.log2(pi) for pi in p if pi > 0)

# Parse DNS queries from tshark output
import subprocess
result = subprocess.run(
    ["tshark", "-r", "malware.pcap", "-T", "fields", "-e", "dns.qry.name",
     "-Y", "dns.flags.response == 0"],
    capture_output=True, text=True
)

domains = set(result.stdout.strip().split('\n'))
print("Suspicious DNS queries (high entropy):")
for domain in domains:
    if domain:
        subdomain = domain.split('.')[0]
        ent = entropy(subdomain)
        if ent > 3.5 and len(subdomain) > 10:
            print(f"  {domain} (entropy: {ent:.2f})")
PYEOF

# Detect DNS tunneling (large TXT responses)
tshark -r malware.pcap -T fields -e dns.qry.name -e dns.txt \
  -Y "dns.resp.type == 16 and dns.resp.len > 100"
Step 3: Analyze HTTP/HTTPS C2 Communication

Examine web-based command-and-control traffic:

bash
# Extract HTTP requests
tshark -r malware.pcap -T fields \
  -e frame.time -e ip.src -e ip.dst -e http.host \
  -e http.request.method -e http.request.uri -e http.user_agent \
  -Y "http.request"

# Extract HTTP response bodies (potential payload downloads)
tshark -r malware.pcap -T fields \
  -e http.host -e http.request.uri -e http.content_type -e tcp.len \
  -Y "http.response and tcp.len > 1000"

# Extract POST data (potential exfiltration)
tshark -r malware.pcap -T fields \
  -e http.host -e http.request.uri -e http.file_data \
  -Y "http.request.method == POST"

# TLS analysis (SNI, JA3 fingerprints)
tshark -r malware.pcap -T fields \
  -e tls.handshake.extensions_server_name \
  -e tls.handshake.ja3 \
  -Y "tls.handshake.type == 1"

# Extract TLS certificate details
tshark -r malware.pcap -T fields \
  -e x509ce.dNSName -e x509af.serialNumber \
  -e x509sat.utf8String \
  -Y "tls.handshake.type == 11"

# Export HTTP objects (downloaded files)
tshark -r malware.pcap --export-objects http,exported_files/
Step 4: Detect Beaconing Patterns

Identify regular periodic communication indicating C2 beaconing:

python
# Beacon detection from PCAP
from scapy.all import rdpcap, IP, TCP
from collections import defaultdict
import statistics

packets = rdpcap("malware.pcap")

# Group connections by destination IP:port
connections = defaultdict(list)
for pkt in packets:
    if IP in pkt and TCP in pkt:
        if pkt[TCP].flags & 0x02:  # SYN flag
            dst = f"{pkt[IP].dst}:{pkt[TCP].dport}"
            connections[dst].append(float(pkt.time))

# Analyze timing intervals for beaconing
print("Beacon Analysis:")
for dst, times in connections.items():
    if len(times) >= 5:
        intervals = [times[i+1] - times[i] for i in range(len(times)-1)]
        avg = statistics.mean(intervals)
        stdev = statistics.stdev(intervals) if len(intervals) > 1 else 0
        jitter = (stdev / avg * 100) if avg > 0 else 0

        if 10 < avg < 3600 and jitter < 30:  # Regular interval with < 30% jitter
            print(f"  [!] {dst}: {len(times)} connections")
            print(f"      Interval: {avg:.1f}s ± {stdev:.1f}s (jitter: {jitter:.1f}%)")
            print(f"      Pattern: LIKELY BEACONING")
Step 5: Generate Network Detection Signatures

Create Suricata/Snort rules from observed traffic patterns:

bash
# Run Suricata against the PCAP for existing signature matches
suricata -r malware.pcap -l suricata_output/ -c /etc/suricata/suricata.yaml

# Review alerts
cat suricata_output/fast.log

# Create custom Suricata rule from observed patterns
cat << 'EOF' > custom_malware.rules
# C2 beacon detection based on observed URI pattern
alert http $HOME_NET any -> $EXTERNAL_NET any (
    msg:"MALWARE MalwareX C2 Beacon";
    flow:established,to_server;
    http.method; content:"POST";
    http.uri; content:"/gate.php?id=";
    http.user_agent; content:"Mozilla/5.0 (compatible; MSIE 10.0)";
    sid:9000001; rev:1;
)

# DNS query for known C2 domain
alert dns $HOME_NET any -> any any (
    msg:"MALWARE MalwareX C2 DNS Query";
    dns.query; content:"update.malicious.com";
    sid:9000002; rev:1;
)

# JA3 hash match for malware TLS client
alert tls $HOME_NET any -> $EXTERNAL_NET any (
    msg:"MALWARE MalwareX JA3 Match";
    ja3.hash; content:"a0e9f5d64349fb13191bc781f81f42e1";
    sid:9000003; rev:1;
)
EOF
Step 6: Extract Files and Artifacts from Traffic

Recover transferred files and embedded data:

bash
# Extract files using Zeek
zeek -r malware.pcap /opt/zeek/share/zeek/policy/frameworks/files/extract-all-files.zeek
ls extract_files/

# Extract files using NetworkMiner (GUI)
# Or use tshark for specific protocol exports
tshark -r malware.pcap --export-objects http,http_objects/
tshark -r malware.pcap --export-objects smb,smb_objects/
tshark -r malware.pcap --export-objects tftp,tftp_objects/

# Hash all extracted files
sha256sum http_objects/* smb_objects/* 2>/dev/null

# Generate Zeek logs for comprehensive metadata
zeek -r malware.pcap
# Output: conn.log, dns.log, http.log, ssl.log, files.log, etc.

Key Concepts

TermDefinition
BeaconingRegular periodic connections from malware to C2 server, identifiable by consistent time intervals and packet sizes
JA3/JA3STLS fingerprinting method creating a hash from ClientHello/ServerHello parameters to uniquely identify malware TLS implementations
DGA (Domain Generation Algorithm)Algorithm generating pseudo-random domain names that malware queries to locate C2 servers, evading static domain blocklists
DNS TunnelingEncoding data in DNS queries and responses to establish a C2 channel or exfiltrate data through DNS infrastructure
Fast FluxDNS technique rapidly rotating IP addresses for a domain to avoid takedown and distribute C2 across many compromised hosts
SNI (Server Name Indication)TLS extension revealing the hostname the client is connecting to; visible even in encrypted HTTPS connections
Network SignatureSuricata/Snort rule matching specific patterns in network traffic (headers, payloads, timing) to detect malicious communications
Show full SKILL.md (252 more words)Show less

Tools & Systems

  • Wireshark: Open-source packet analyzer for deep interactive inspection of network traffic at the protocol level
  • Zeek: Network analysis framework generating structured metadata logs (conn, dns, http, ssl) from live or captured traffic
  • Suricata: High-performance network IDS/IPS for signature-based detection with Lua scripting for custom detection logic
  • NetworkMiner: Network forensic analysis tool for extracting files, images, and credentials from PCAP files
  • Scapy: Python packet manipulation library for programmatic packet analysis, beacon detection, and protocol decoding

Common Scenarios

Scenario: Decoding a Custom Binary C2 Protocol

Context: Malware communicates with its C2 server using a custom binary protocol over TCP port 8443. Standard HTTP analysis yields no results. The protocol structure needs to be reverse engineered from the PCAP.

Approach:

  1. Filter the PCAP for TCP port 8443 conversations and follow the TCP stream
  2. Identify the message framing (length prefix, delimiter, fixed-size headers)
  3. Compare multiple messages to identify static header fields vs variable data fields
  4. Cross-reference with reverse engineering findings from Ghidra (if the binary was analyzed)
  5. Write a Wireshark dissector or Scapy parser for the custom protocol
  6. Create Suricata rules matching the static header bytes for network detection
  7. Document the full protocol specification for threat intelligence sharing

Pitfalls:

  • Analyzing only the first few packets; some C2 protocols change behavior after initial handshake
  • Not decrypting TLS traffic when the sandbox has MITM capabilities
  • Confusing legitimate CDN or cloud traffic with C2 (validate destination IPs)
  • Missing C2 traffic that uses DNS or ICMP instead of TCP/UDP

Output Format

MALWARE NETWORK TRAFFIC ANALYSIS
===================================
PCAP File:        malware_sandbox.pcap
Duration:         300 seconds
Total Packets:    12,847
Total Bytes:      4.2 MB

DNS ACTIVITY
Total Queries:    47
DGA Detected:     Yes (23 high-entropy queries to .com TLD)
Tunneling:        No
Resolved C2:      update.malicious[.]com -> 185.220.101[.]42

C2 COMMUNICATION
Protocol:         HTTPS (TLS 1.2)
Server:           185.220.101[.]42:443
SNI:              update.malicious[.]com
JA3 Hash:         a0e9f5d64349fb13191bc781f81f42e1
Beacon Interval:  60.2s ± 6.8s (11.3% jitter)
Total Sessions:   237
Data Sent:        147 MB
Data Received:    2.3 MB
Certificate:      CN=update.malicious[.]com (self-signed, expired)

PAYLOAD DOWNLOADS
GET /payload.dll from compromised-site[.]com
  Size: 98,304 bytes
  SHA-256: abc123def456...
  Content-Type: application/octet-stream

EXFILTRATION
Method:           HTTPS POST to /gate.php
Content-Type:     application/octet-stream
Average Size:     15,432 bytes per request
Total Volume:     147 MB over 4 hours

SURICATA ALERTS
[1:2028401] ET MALWARE Generic C2 Beacon Pattern
[1:2028500] ET POLICY Self-Signed Certificate

GENERATED SIGNATURES
SID 9000001: MalwareX HTTP beacon pattern
SID 9000002: MalwareX DNS C2 domain
SID 9000003: MalwareX JA3 TLS fingerprint

© 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-of-malware of mukul975/Anthropic-Cybersecurity-Skills.

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

Open the folder on GitHubat commit 54a7988

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Works with

Questions about Analyzing Network Traffic Of Malware

What does Analyzing Network Traffic Of Malware do?

Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement…. Analyzing Network Traffic Of Malware is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata.

When should I use Analyzing Network Traffic Of Malware?

Analyzing Network Traffic Of Malware fits situations like: tasks that involve Red teaming and adversary simulation; tasks that involve Network security; tasks that involve Incident response.

How do I install Analyzing Network Traffic Of Malware in Claude Code?

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

How do I install Analyzing Network Traffic Of Malware in Codex?

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

Can I use Analyzing Network Traffic Of Malware 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-of-malware -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-of-malware, .gemini/skills/analyzing-network-traffic-of-malware, .github/skills/analyzing-network-traffic-of-malware and .opencode/skills/analyzing-network-traffic-of-malware in your project.

What does Analyzing Network Traffic Of Malware need to run?

Going by SKILL.md and its folder, Analyzing Network Traffic Of Malware needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Analyzing Network Traffic Of Malware access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Analyzing Network Traffic Of Malware 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 Of Malware use?

Analyzing Network Traffic Of Malware 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 Of Malware use?

About 3k tokens (SKILL.md is roughly 12k 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 732 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Network Traffic Of Malware?

Skills that share tags, products or a category with Analyzing Network Traffic Of Malware: Incident Response Network (LeoYeAI/openclaw-master-skills, 2.2k stars), Protocol Reverse Engineering (wshobson/agents, 40k stars), Traffic Analysis Pcap (yaklang/hack-skills, 2.4k stars) and Azure Kusto Irql (microsoft/GitHub-Copilot-for-Azure, 255 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 Of Malware?

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.