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analyzing-command-and-control-communication

Makes your agent analyze the communication of malicious programs over the internet. It discovers how they send commands and how data is hidden. It's useful for creating defense systems against attacks.

What it does

This tool helps understand how a malicious program (malware) communicates with its control server. It analyzes network traffic to uncover patterns, commands, and how information is sent.

When to use it

  • ✓When you find a suspicious program and want to see how it 'talks' to the internet.
  • ✓When creating defenses to detect specific malicious programs.
  • ✓To map out the entire control network of a malware.

Example of what to ask your AI

“Analyze this network traffic to discover how the malware is communicating.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/skills/analyzing-command-and-control-communication/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/skills/analyzing-command-and-control-communication/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The tool examines the data the malicious program sends and receives over the network. It looks for repetitive patterns and tries to decipher the language used to send commands and receive information.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-command-and-control-communication

Or download just the file:

curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/analyzing-command-and-control-communication/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Analyzing Command-and-Control Communication

When to Use

  • Reverse engineering a malware sample has revealed network communication that needs protocol analysis
  • Building network-level detection signatures for a specific C2 framework (Cobalt Strike, Metasploit, Sliver)
  • Mapping C2 infrastructure including primary servers, fallback domains, and dead drops
  • Analyzing encrypted or encoded C2 traffic to understand the command set and data format
  • Attributing malware to a threat actor based on C2 infrastructure patterns and tooling

Do not use for general network anomaly detection; this is specifically for understanding known or suspected C2 protocols from malware analysis.

Prerequisites

  • PCAP capture of malware network traffic (from sandbox, network tap, or full packet capture)
  • Wireshark/tshark for packet-level analysis
  • Reverse engineering tools (Ghidra, dnSpy) for understanding C2 code in the malware binary
  • Python 3.8+ with scapy, dpkt, and requests for protocol analysis and replay
  • Threat intelligence databases for C2 infrastructure correlation (VirusTotal, Shodan, Censys)
  • JA3/JA3S fingerprint databases for TLS-based C2 identification

Workflow

Step 1: Identify the C2 Channel

Determine the protocol and transport used for C2 communication:

C2 Communication Channels:
━━━━━━━━━━━━━━━━━━━━━━━━━
HTTP/HTTPS:     Most common; uses standard web traffic to blend in
                Indicators: Regular POST/GET requests, specific URI patterns, custom headers

DNS:            Tunneling data through DNS queries and responses
                Indicators: High-volume TXT queries, long subdomain names, high entropy

Custom TCP/UDP: Proprietary binary protocol on non-standard port
                Indicators: Non-HTTP traffic on high ports, unknown protocol

ICMP:           Data encoded in ICMP echo/reply payloads
                Indicators: ICMP packets with large or non-standard payloads

WebSocket:      Persistent bidirectional connection for real-time C2
                Indicators: WebSocket upgrade followed by binary frames

Cloud Services: Using legitimate APIs (Telegram, Discord, Slack, GitHub)
                Indicators: API calls to cloud services from unexpected processes

Email:          SMTP/IMAP for C2 commands and data exfiltration
                Indicators: Automated email operations from non-email processes

Step 2: Analyze Beacon Pattern

Characterize the periodic communication pattern:

from scapy.all import rdpcap, IP, TCP
from collections import defaultdict
import statistics
import json

packets = rdpcap("c2_traffic.pcap")

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

# Analyze each destination for beaconing
for dst, times in sorted(connections.items()):
    if len(times) < 3:
        continue

    intervals = [times[i+1] - times[i] for i in range(len(times)-1)]
    avg_interval = statistics.mean(intervals)
    stdev = statistics.stdev(intervals) if len(intervals) > 1 else 0
    jitter_pct = (stdev / avg_interval * 100) if avg_interval > 0 else 0
    duration = times[-1] - times[0]

    beacon_data = {
        "destination": dst,
        "connections": len(times),
        "duration_seconds": round(duration, 1),
        "avg_interval_seconds": round(avg_interval, 1),
        "stdev_seconds": round(stdev, 1),
        "jitter_percent": round(jitter_pct, 1),
        "is_beacon": 5 < avg_interval < 7200 and jitter_pct < 25,
    }

    if beacon_data["is_beacon"]:
        print(f"[!] BEACON DETECTED: {dst}")
        print(f"    Interval: {avg_interval:.0f}s +/- {stdev:.0f}s ({jitter_pct:.0f}% jitter)")
        print(f"    Sessions: {len(times)} over {duration:.0f}s")

Step 3: Decode C2 Protocol Structure

Reverse engineer the message format from captured traffic:

# HTTP-based C2 protocol analysis
import dpkt
import base64

with open("c2_traffic.pcap", "rb") as f:
    pcap = dpkt.pcap.Reader(f)

for ts, buf in pcap:
    eth = dpkt.ethernet.Ethernet(buf)
    if not isinstance(eth.data, dpkt.ip.IP):
        continue
    ip = eth.data
    if not isinstance(ip.data, dpkt.tcp.TCP):
        continue
    tcp = ip.data

    if tcp.dport == 80 or tcp.dport == 443:
        if len(tcp.data) > 0:
            try:

…
FAQ

What is 'C2'?

C2 stands for 'Command and Control'. It's how a malicious program communicates with the attacker controlling it.

Do I need to be a programmer to use this?

You don't need to be an experienced programmer. The tool is designed to help understand the communication even if you don't write code.

What is a 'beacon pattern'?

It's the pattern of when and how often the malware communicates with the control server. It's like a regular signal it sends out.

Details

Original description by the author

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback domains, dead drops). Use after reverse engineering reveals network traffic needing protocol analysis or when building detection signatures for a framework like Cobalt Strike, Metasploit, or Sliver. '

repo: mukul975/Anthropic-Cybersecurity-Skills

Popularity

GitHub stars
35k
Ranking position
#5444
License
Apache-2.0
Updated
8/31/2026

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