analyzing-network-traffic-of-malware

analyzing-network-traffic-of-malware is a skill for Claude Code from mukul975/Anthropic-Cybersecurity-Skills. It costs 81 tokens per session (3,131 once invoked), scanned A, original, Apache-2.0.

A malware investigation guide for examining network traffic captured while malicious software runs in a sandbox or during an incident. It focuses on command-and-control traffic, data theft, downloads, and movement between systems using packet and network-analysis tools.

In plain words
What is it for?
Use it to inspect PCAP files, decode malware communication, identify data exfiltration and downloads, investigate DNS tunneling or changing domains, and create Suricata or Snort detection signatures.
Why use it?
It turns a packet capture into evidence about what the malware contacted, sent, received, or tried to do. This helps distinguish malicious communication from normal traffic and supports detection rules.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the cybersecurity-skills plugin — 56 skills shipped together

Good fit Use it to inspect PCAP files, decode malware communication, identify data exfiltration and downloads, investigate DNS tunneling or changing domains, and create Suricata or Snort detection signatures.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware
About the project

Anthropic Cybersecurity Skills is a library of structured cybersecurity procedures for AI agents, covering security domains and mappings to established security frameworks. It is for authorized security analysis, penetration testing, incident response, research, defense, and education across compatible AI platforms. The catalogue entries package parts of this library as agent skills, instructions, or a plugin.

mukul975/Anthropic-Cybersecurity-Skills · 32,631 stars · on GitHub · mahipal.engineer

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-traffic-of-malware
Clone the repo
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills

Made for: Claude Code.

Or install cybersecurity-skills, the plugin that ships this one along with the rest of its 56 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyzing-network-traffic-of-malware

README.md
[![agentmods](https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware/github.svg)](https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware)
Your own site
<a href="https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for analyzing-network-traffic-of-malware

Your own site · 80×15
<a href="https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-network-traffic-of-malware.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,131 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 6 Apr 2026
  • Snyk pass 6 Apr 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Output Handling · line 97
    Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.
    Fix: Validate and sanitize all model output before using it in downstream contexts. Use parameterized queries for SQL, shell quoting for commands, and HTML encoding for web output.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00081 $0.03131
Opus 5 $0.00041 $0.01566
Sonnet 5 $0.00016 $0.00626
Haiku 4.5 $0.00008 $0.00313

Measured 13d ago against content hash 83c6be312c1b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

analyzing-network-traffic-of-malware scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

result = subprocess.run(
Origin

Copies of this mod

6 near-identical copies found in the catalogue:

skills/analyzing-network-traffic-of-malware/SKILL.md · 341 lines

How it starts

The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

# 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:

# 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"

Read the full file on GitHub · 341 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 341 lines · 81 tokens per session scan A 83c6be312c1b

Subscribe to this mod's changes

analyzing-network-traffic-of-malware is a skill published in the GitHub repository mukul975/Anthropic-Cybersecurity-Skills (32,631 stars, last pushed 12d ago), licensed Apache-2.0. It adds 81 tokens to every session and 3,131 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

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