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Analyzing Command And Control Communication

  • 408 installs
  • 27.3k repo stars
  • Updated August 2, 2026
  • mukul975/anthropic-cybersecurity-skills

Analyzing Command and Control Communication is an agent skill that structures how your coding agent reasons about malicious command-and-control traffic and channels.

About

Analyzing Command and Control Communication is a security-focused agent skill from the Anthropic cybersecurity skills collection, intended for solo builders and small teams who need structured help reasoning about how malware talks to external infrastructure. Use it when you are reviewing logs, designing detections, or documenting suspicious beaconing during ship-phase security work—not for casual feature coding. The catalog entry currently surfaces license metadata more than procedural SKILL.md text, so you should treat it as a specialized procedural companion your agent loads alongside concrete log samples and your own runbooks. It matters because misclassified C2 noise wastes nights and misses real breaches; a named skill nudges the agent toward consistent MITRE-aligned questions instead of generic “check the network” advice. Confirm scope and steps in the upstream repo before relying on it in production incident response.

  • Frames investigation of attacker C2 channels (beacons, DNS, HTTP/S) for agent-assisted triage
  • Fits security skill packs aimed at blue-team and defensive coding workflows
  • Pairs with broader Anthropic cybersecurity skills for layered incident response
  • Apache-2.0 licensed package suitable for auditing before enabling network-heavy agent steps

Analyzing Command And Control Communication by the numbers

  • 408 all-time installs (skills.sh)
  • +23 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #549 of 2,203 Security skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs408
repo stars27.3k
Security audit2 / 3 scanners passed
Last updatedAugust 2, 2026
Repositorymukul975/anthropic-cybersecurity-skills

What it does

Guide your agent through structured analysis of command-and-control (C2) traffic and infrastructure when you are hardening or investigating compromised systems.

Who is it for?

Best when you're doing your own security reviews and want agent-guided C2 analysis checklists during launch prep or post-incident triage.

Skip if: Skip if you have no logs or network context to analyze, or teams that need certified IR vendors instead of agent-assisted reasoning—verify full SKILL.md and tooling in the source repo first.

When should I use this skill?

When investigating suspicious network behavior, designing C2 detections, or documenting attacker communication patterns during security review.

What you get

You get a consistent analysis frame for C2 indicators and communication patterns you can fold into detection notes, tickets, or hardening tasks.

  • C2 analysis notes
  • Detection or hardening follow-up tasks

Files

SKILL.mdMarkdownGitHub ↗

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:
                http = dpkt.http.Request(tcp.data)
                print(f"\n--- C2 REQUEST ---")
                print(f"Method: {http.method}")
                print(f"URI: {http.uri}")
                print(f"Headers: {dict(http.headers)}")
                if http.body:
                    print(f"Body ({len(http.body)} bytes):")
                    # Try Base64 decode
                    try:
                        decoded = base64.b64decode(http.body)
                        print(f"  Decoded: {decoded[:200]}")
                    except:
                        print(f"  Raw: {http.body[:200]}")
            except:
                pass

Step 4: Identify C2 Framework

Match observed patterns to known C2 frameworks:

Known C2 Framework Signatures:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Cobalt Strike:
  - Default URIs: /pixel, /submit.php, /___utm.gif, /ca, /dpixel
  - Malleable C2 profiles customize all traffic characteristics
  - JA3: varies by profile, catalog at ja3er.com
  - Watermark in beacon config (unique per license)
  - Config extraction: use CobaltStrikeParser or 1768.py

Metasploit/Meterpreter:
  - Default staging URI patterns: random 4-char checksum
  - Reverse HTTP(S) handler patterns
  - Meterpreter TLV (Type-Length-Value) protocol structure

Sliver:
  - mTLS, HTTP, DNS, WireGuard transport options
  - Protobuf-encoded messages
  - Unique implant ID in communication

Covenant:
  - .NET-based C2 framework
  - HTTP with customizable profiles
  - Task-based command execution

PoshC2:
  - PowerShell/C# based
  - HTTP with encrypted payloads
  - Cookie-based session management
# Extract Cobalt Strike beacon configuration from PCAP or sample
python3 << 'PYEOF'
# Using CobaltStrikeParser (pip install cobalt-strike-parser)
from cobalt_strike_parser import BeaconConfig

try:
    config = BeaconConfig.from_file("suspect.exe")
    print("Cobalt Strike Beacon Configuration:")
    for key, value in config.items():
        print(f"  {key}: {value}")
except Exception as e:
    print(f"Not a Cobalt Strike beacon or parse error: {e}")
PYEOF

Step 5: Map C2 Infrastructure

Document the full C2 infrastructure and failover mechanisms:

# Infrastructure mapping
import requests
import json

c2_indicators = {
    "primary_c2": "185.220.101.42",
    "domains": ["update.malicious.com", "backup.evil.net"],
    "ports": [443, 8443],
    "failover_dns": ["ns1.malicious-dns.com"],
}

# Enrich with Shodan
def shodan_lookup(ip, api_key):
    resp = requests.get(f"https://api.shodan.io/shodan/host/{ip}?key={api_key}")
    if resp.status_code == 200:
        data = resp.json()
        return {
            "ip": ip,
            "ports": data.get("ports", []),
            "os": data.get("os"),
            "org": data.get("org"),
            "asn": data.get("asn"),
            "country": data.get("country_code"),
            "hostnames": data.get("hostnames", []),
            "last_update": data.get("last_update"),
        }
    return None

# Enrich with passive DNS
def pdns_lookup(domain):
    # Using VirusTotal passive DNS
    resp = requests.get(
        f"https://www.virustotal.com/api/v3/domains/{domain}/resolutions",
        headers={"x-apikey": VT_API_KEY}
    )
    if resp.status_code == 200:
        data = resp.json()
        resolutions = []
        for r in data.get("data", []):
            resolutions.append({
                "ip": r["attributes"]["ip_address"],
                "date": r["attributes"]["date"],
            })
        return resolutions
    return []

Step 6: Create Network Detection Signatures

Build detection rules based on analyzed C2 characteristics:

# Suricata rules for the analyzed C2
cat << 'EOF' > c2_detection.rules
# HTTP beacon pattern
alert http $HOME_NET any -> $EXTERNAL_NET any (
    msg:"MALWARE MalwareX C2 HTTP Beacon";
    flow:established,to_server;
    http.method; content:"POST";
    http.uri; content:"/gate.php"; startswith;
    http.header; content:"User-Agent: Mozilla/5.0 (compatible; MSIE 10.0)";
    threshold:type threshold, track by_src, count 5, seconds 600;
    sid:9000010; rev:1;
)

# JA3 fingerprint match
alert tls $HOME_NET any -> $EXTERNAL_NET any (
    msg:"MALWARE MalwareX TLS JA3 Fingerprint";
    ja3.hash; content:"a0e9f5d64349fb13191bc781f81f42e1";
    sid:9000011; rev:1;
)

# DNS beacon detection (high-entropy subdomain)
alert dns $HOME_NET any -> any any (
    msg:"MALWARE Suspected DNS C2 Tunneling";
    dns.query; pcre:"/^[a-z0-9]{20,}\./";
    threshold:type threshold, track by_src, count 10, seconds 60;
    sid:9000012; rev:1;
)

# Certificate-based detection
alert tls $HOME_NET any -> $EXTERNAL_NET any (
    msg:"MALWARE MalwareX Self-Signed C2 Certificate";
    tls.cert_subject; content:"CN=update.malicious.com";
    sid:9000013; rev:1;
)
EOF

Key Concepts

TermDefinition
BeaconingPeriodic check-in communication from malware to C2 server at regular intervals, often with jitter to avoid pattern detection
JitterRandomization applied to beacon interval (e.g., 60s +/- 15%) to make the timing pattern less predictable and harder to detect
Malleable C2Cobalt Strike feature allowing operators to customize all aspects of C2 traffic (URIs, headers, encoding) to mimic legitimate services
Dead DropIntermediate location (paste site, cloud storage, social media) where C2 commands are posted for the malware to retrieve
Domain FrontingUsing a trusted CDN domain in the TLS SNI while routing to a different backend, making C2 traffic appear to go to a legitimate service
Fast FluxRapidly changing DNS records for C2 domains to distribute across many IPs and resist takedown efforts
C2 FrameworkSoftware toolkit providing C2 server, implant generator, and operator interface (Cobalt Strike, Metasploit, Sliver, Covenant)

Tools & Systems

  • Wireshark: Packet analyzer for detailed C2 protocol analysis at the packet level
  • RITA (Real Intelligence Threat Analytics): Open-source tool analyzing Zeek logs for beacon detection and DNS tunneling
  • CobaltStrikeParser: Tool extracting Cobalt Strike beacon configuration from samples and memory dumps
  • JA3/JA3S: TLS fingerprinting method for identifying C2 frameworks by their TLS implementation characteristics
  • Shodan/Censys: Internet scanning platforms for mapping C2 infrastructure and identifying related servers

Common Scenarios

Scenario: Reverse Engineering a Custom C2 Protocol

Context: A malware sample communicates with its C2 server using an unknown binary protocol over TCP port 8443. The protocol needs to be decoded to understand the command set and build detection signatures.

Approach: 1. Filter PCAP for TCP port 8443 conversations and extract the TCP streams 2. Analyze the first few exchanges to identify the handshake/authentication mechanism 3. Map the message structure (length prefix, type field, payload encoding) 4. Cross-reference with Ghidra disassembly of the send/receive functions in the malware 5. Identify the command dispatcher and document each command code's function 6. Build a protocol decoder in Python for ongoing traffic analysis 7. Create Suricata rules matching the protocol handshake or static header bytes

Pitfalls:

  • Assuming the protocol is static; some C2 frameworks negotiate encryption during the handshake
  • Not capturing enough traffic to see all command types (some commands are rare)
  • Missing fallback C2 channels (DNS, ICMP) that activate when the primary channel fails
  • Confusing encrypted payload data with the protocol framing structure

Output Format

C2 COMMUNICATION ANALYSIS REPORT
===================================
Sample:           malware.exe (SHA-256: e3b0c44...)
C2 Framework:     Cobalt Strike 4.9

BEACON CONFIGURATION
C2 Server:        hxxps://185.220.101[.]42/updates
Beacon Type:      HTTPS (reverse)
Sleep:            60 seconds
Jitter:           15%
User-Agent:       Mozilla/5.0 (Windows NT 10.0; Win64; x64)
URI (GET):        /dpixel
URI (POST):       /submit.php
Watermark:        1234567890

PROTOCOL ANALYSIS
Transport:        HTTPS (TLS 1.2)
JA3 Hash:         a0e9f5d64349fb13191bc781f81f42e1
Certificate:      CN=Microsoft Update (self-signed)
Encoding:         Base64 with XOR key 0x69
Command Format:   [4B length][4B command_id][payload]

COMMAND SET
0x01 - Sleep          Change beacon interval
0x02 - Shell          Execute cmd.exe command
0x03 - Download       Transfer file from C2
0x04 - Upload         Exfiltrate file to C2
0x05 - Inject         Process injection
0x06 - Keylog         Start keylogger
0x07 - Screenshot     Capture screen

INFRASTRUCTURE
Primary:          185.220.101[.]42 (AS12345, Hosting Co, NL)
Failover:         91.215.85[.]17 (AS67890, VPS Provider, RU)
DNS:              update.malicious[.]com -> 185.220.101[.]42
Registrar:        NameCheap
Registration:     2025-09-01

DETECTION SIGNATURES
SID 9000010:      HTTP beacon pattern
SID 9000011:      JA3 TLS fingerprint
SID 9000013:      C2 certificate match

Related skills

How it compares

Use as a focused security procedure skill, not a generic debugging or DevOps deploy playbook.

FAQ

Who is analyzing-command-and-control-communication for?

Developers handling their own security reviews, side-project APIs, or small prod fleets who want an agent skill oriented to C2 and beacon analysis rather than feature development.

When should I use analyzing-command-and-control-communication?

Use it in Ship (security) when reviewing suspicious traffic before launch, after a breach scare, or in Operate when correlating monitoring alerts with possible C2—always with real telemetry you can share with the agent.

Is analyzing-command-and-control-communication safe to install?

Review the Security Audits panel on this Prism page and the upstream Apache-2.0 package; do not grant broad network or shell access until you have read the full skill source and scoped agent permissions.

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