hunting-for-command-and-control-beaconing
Makes your agent detect 'malware' communication patterns on the network. It helps identify infected computers communicating with malicious servers.
What it does
This tool helps find computers on your network that might be secretly communicating with criminals. It looks for unusual communication patterns that indicate a malicious program is sending and receiving commands.
When to use it
- ✓When you want to search for infected computers on the network.
- ✓If you've heard that criminals are using specific tools.
- ✓When investigating network connections that seem strange or suspicious.
Example of what to ask your AI
“Look for signs of secret communication from computers on my network.”
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
Ready-to-paste message
How it works
The tool analyzes network traffic to identify regular and suspicious communication patterns. It checks if the destinations of these communications are known to be dangerous and if computers are communicating in an unusual way.
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 hunting-for-command-and-control-beaconingOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/hunting-for-command-and-control-beaconing/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Hunting for Command and Control Beaconing
When to Use
- When proactively hunting for compromised systems in the network
- After threat intel indicates C2 frameworks targeting your industry
- When investigating periodic outbound connections to suspicious domains
- During incident response to identify active C2 channels
- When DNS query logs show unusual patterns to specific domains
Prerequisites
- Network proxy/firewall logs with full URL and timing data
- DNS query logs (passive DNS, DNS server logs, or Sysmon Event ID 22)
- Zeek/Bro network connection logs or NetFlow data
- SIEM with statistical analysis capabilities (Splunk, Elastic)
- Threat intelligence feeds for domain/IP reputation
Workflow
- Identify Beaconing Characteristics: Define what constitutes beaconing (regular intervals, small payload sizes, consistent destinations, jitter patterns).
- Collect Network Telemetry: Aggregate proxy logs, DNS queries, and connection metadata for analysis.
- Apply Frequency Analysis: Identify connections with regular intervals using statistical methods (standard deviation, coefficient of variation).
- Filter Known-Good Traffic: Exclude legitimate periodic traffic (Windows Update, AV updates, heartbeat services, NTP).
- Analyze Domain/IP Reputation: Check identified beaconing destinations against threat intel, WHOIS data, and certificate transparency logs.
- Investigate Endpoint Context: Correlate beaconing activity with process creation, user context, and file system changes on source endpoints.
- Confirm and Respond: Validate C2 activity, block communication, and initiate incident response.
Key Concepts
| Concept | Description |
|---|---|
| T1071 | Application Layer Protocol (HTTP/HTTPS/DNS C2) |
| T1071.001 | Web Protocols (HTTP/S beaconing) |
| T1071.004 | DNS (DNS tunneling C2) |
| T1573 | Encrypted Channel |
| T1572 | Protocol Tunneling |
| T1568 | Dynamic Resolution (DGA, fast-flux) |
| T1132 | Data Encoding in C2 |
| T1095 | Non-Application Layer Protocol |
| Beacon Interval | Time between C2 check-ins |
| Jitter | Random variation in beacon interval |
| DGA | Domain Generation Algorithm |
| Fast-Flux | Rapidly changing DNS resolution |
Tools & Systems
| Tool | Purpose |
|---|---|
| RITA (Real Intelligence Threat Analytics) | Automated beacon detection in Zeek logs |
| Splunk | Statistical beacon analysis with SPL |
| Elastic Security | ML-based anomaly detection for beaconing |
| Zeek/Bro | Network connection metadata collection |
| Suricata | Network IDS with JA3/JA4 fingerprinting |
| VirusTotal | Domain and IP reputation checking |
| PassiveDNS | Historical DNS resolution data |
| Flare | C2 profile detection |
Common Scenarios
- Cobalt Strike Beacon: HTTP/HTTPS beaconing with configurable sleep time and jitter to malleable C2 profiles.
- DNS Tunneling C2: Data exfiltration and command receipt via encoded DNS TXT/CNAME queries to attacker-controlled domains.
- Sliver C2 over HTTPS: Modern C2 framework using HTTPS with configurable beacon intervals and domain fronting.
- DGA-based C2: Malware generating random domains daily, with adversary registering upcoming domains for C2.
- Legitimate Service Abuse: C2 over legitimate cloud services (Azure, AWS, Slack, Discord, Telegram).
Output Format
Hunt ID: TH-C2-[DATE]-[SEQ]
Source IP: [Internal IP]
Source Host: [Hostname]
Destination: [Domain/IP]
Protocol: [HTTP/HTTPS/DNS/Custom]
Beacon Interval: [Average seconds]
Jitter: [Percentage]
Connection Count: [Total connections]
Data Volume: [Bytes sent/received]
First Seen: [Timestamp]
Last Seen: [Timestamp]
Domain Age: [Days]
TI Match: [Yes/No - source]
Risk Level: [Critical/High/Medium/Low]
FAQ
What is 'beaconing'?
'Beaconing' is like a radio signal that malicious software uses to secretly communicate with the attacker. It sends signals at regular intervals to check for new commands or to send information.
What is 'jitter'?
'Jitter' is a small, random variation in the timing between communications. Attackers use this to try and disguise regular patterns, making it harder for security tools to detect the communication.
Do I need technical knowledge to use this?
This tool is designed for people monitoring network security. While the technical details are complex, the goal is to simplify threat detection.
Details
Original description by the author
Detect C2 beaconing patterns in network traffic using frequency analysis, jitter detection, and domain reputation to identify compromised endpoints communicating with adversary infrastructure.
Popularity
- GitHub stars
- 35k
- Ranking position
- #5788
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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