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Analysis & spreadsheets35k · #5786

hunting-for-beaconing-with-frequency-analysis

Makes your agent identify 'malware' communication patterns by analyzing network traffic. It helps detect suspicious activities and connections to command and control servers.

What it does

This tool helps find computers on your network that might be secretly communicating with a malicious server. It does this by looking for communication patterns that repeat at regular intervals, like a 'signal' sent back to a 'commander'.

When to use it

  • ✓When you suspect a computer might be compromised.
  • ✓If you've received warnings about new threats that could affect your network.
  • ✓When you see computers connecting to strange places on the internet at regular intervals.

Example of what to ask your AI

“Look for signs of secret communication on our 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

https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/skills/hunting-for-beaconing-with-frequency-analysis/SKILL.md

Ready-to-paste message

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

The tool analyzes network connection logs to find repeating communication patterns. It calculates the time between connections and checks for a regular pattern, while ignoring normal communications like software updates.

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-beaconing-with-frequency-analysis

Or download just the file:

curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/hunting-for-beaconing-with-frequency-analysis/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Hunting for Beaconing with Frequency Analysis

When to Use

  • When proactively searching for compromised endpoints calling back to C2 infrastructure
  • After threat intelligence reports indicate active C2 frameworks targeting your sector
  • When network logs show periodic outbound connections to unfamiliar destinations
  • During purple team exercises validating C2 detection capabilities
  • When investigating a potential breach and need to identify active C2 channels

Prerequisites

  • Network proxy/firewall logs with timestamps and destination data (minimum 24 hours)
  • Zeek conn.log, dns.log, and ssl.log or equivalent NetFlow/IPFIX data
  • SIEM platform with statistical analysis capability (Splunk, Elastic, Microsoft Sentinel)
  • RITA (Real Intelligence Threat Analytics) or AC-Hunter for automated beacon analysis
  • Threat intelligence feeds for domain/IP reputation enrichment

Workflow

  1. Define Beacon Parameters: Establish detection thresholds -- coefficient of variation (CV) below 0.20 indicates strong periodicity, minimum 50 connections over 24 hours, average interval between 30 seconds and 24 hours.
  2. Collect Network Telemetry: Aggregate proxy logs, DNS queries, firewall connection logs, and Zeek metadata into the analysis platform.
  3. Calculate Connection Intervals: For each source-destination pair, compute the time delta between consecutive connections and derive mean interval, standard deviation, and CV.
  4. Apply Jitter Analysis: Sophisticated C2 frameworks like Cobalt Strike add jitter (randomness) to beacon intervals. The Sunburst backdoor beaconed every 15 minutes plus/minus 90 seconds. Analyze jitter patterns to detect even randomized beaconing.
  5. Filter Legitimate Periodic Traffic: Exclude known-good beaconing sources including Windows Update, antivirus definition updates, NTP synchronization, SaaS heartbeat services, and CDN health checks.
  6. Analyze Data Size Consistency: C2 heartbeat packets typically have consistent payload sizes. Calculate the CV of bytes transferred per connection -- low variance suggests automated communication.
  7. Enrich with Threat Intelligence: Check identified beaconing destinations against VirusTotal, WHOIS registration data (flag domains under 30 days old), certificate transparency logs, and passive DNS history.
  8. Correlate with Endpoint Telemetry: Map beaconing source IPs to endpoint hostnames via DHCP logs, then correlate with process creation events (Sysmon Event ID 1, 3) to identify the responsible process.
  9. Score and Prioritize: Assign risk scores based on CV value, domain age, TI matches, data size consistency, and suspicious port usage. Escalate high-confidence findings.

Key Concepts

Concept Description
T1071.001 Application Layer Protocol: Web Protocols -- HTTP/HTTPS beaconing
T1071.004 Application Layer Protocol: DNS -- DNS-based C2 tunneling
T1573 Encrypted Channel -- TLS/SSL encrypted C2 communication
T1568.002 Dynamic Resolution: Domain Generation Algorithms
Coefficient of Variation Standard deviation divided by mean; values below 0.20 indicate periodicity
Jitter Random variation added to beacon interval to evade detection
RITA Beacon Score Composite score from connection regularity, data size consistency, and connection count
JA3/JA4 Fingerprinting TLS client fingerprinting to identify C2 framework signatures
Fast-Flux DNS Rapidly changing DNS resolution used to protect C2 infrastructure

Tools & Systems

Tool Purpose
RITA (Real Intelligence Threat Analytics) Automated beacon scoring from Zeek logs
AC-Hunter Commercial threat hunting platform with beacon detection
Splunk SPL-based statistical beacon analysis with streamstats
Elastic Security ML anomaly detection for periodic network behavior
Zeek Network metadata collection (conn.log, dns.log, ssl.log)
Suricata Network IDS with JA3/JA4 TLS fingerprint extraction
FLARE C2 profile and beacon pattern detection
VirusTotal Domain and IP reputation enrichment

Detection Queries

Splunk -- HTTP/S Beacon Frequency Analysis

index=proxy OR index=firewall
| where NOT match(dest, "(?i)(microsoft|google|amazonaws|cloudflare|akamai)")
| bin _time span=1s
| stats count by src_ip dest _time
| streamstats current=f last(_time) as prev_time by src_ip dest
| eval interval=_time-prev_time

…
FAQ

What is 'beaconing'?

It's when an infected computer sends regular signals to a control server, like a beacon flashing to guide a ship.

What is 'jitter'?

It's a small random variation in the timing between signals. Hackers use this to try and disguise the regular pattern and avoid detection.

Why do I need network logs for at least 24 hours?

To have enough data to identify repeating communication patterns over time, distinguishing them from normal, sporadic activity.

Details

Original description by the author

Identify command-and-control beaconing patterns in network traffic by applying statistical frequency analysis, jitter calculation, and coefficient of variation scoring to detect periodic callbacks from compromised endpoints.

repo: mukul975/Anthropic-Cybersecurity-Skills

Popularity

GitHub stars
35k
Ranking position
#5786
License
Apache-2.0
Updated
8/31/2026

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