performing-dns-tunneling-detection

name	performing-dns-tunneling-detection
description	Detects DNS tunneling by computing Shannon entropy of DNS query names, analyzing query length distributions, inspecting TXT record payloads, and identifying high subdomain cardinality. Uses scapy for packet capture analysis and statistical methods to distinguish legitimate DNS from covert channels. Use when hunting for data exfiltration.
domain	cybersecurity
subdomain	security-operations
tags	["dns-tunneling","exfiltration-detection","shannon-entropy","dns-analysis","threat-detection","security-operations"]
version	1.0
author	mahipal
license	Apache-2.0
nist_csf	["DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02"]
mitre_attack	["T1078","T1190","T1059","T1048","T1041"]
source	https://github.com/mukul975/Anthropic-Cybersecurity-Skills
source_commit	04450304b12645cb2b974ab96d28c0664758a88d

Performing DNS Tunneling Detection

When to Use

• When conducting security assessments that involve performing dns tunneling detection
• When following incident response procedures for related security events
• When performing scheduled security testing or auditing activities
• When validating security controls through hands-on testing

Prerequisites

• Familiarity with security operations concepts and tools
• Access to a test or lab environment for safe execution
• Python 3.8+ with required dependencies installed
• Appropriate authorization for any testing activities

Instructions

Analyze DNS traffic for indicators of DNS tunneling using entropy analysis and statistical methods on query name characteristics.

import math
from collections import Counter

def shannon_entropy(data):
    if not data:
        return 0
    counter = Counter(data)
    length = len(data)
    return -sum((c/length) * math.log2(c/length) for c in counter.values())

# Legitimate domain: low entropy (~3.0-3.5)
print(shannon_entropy("www.google.com"))
# DNS tunnel: high entropy (~4.0-5.0)
print(shannon_entropy("aGVsbG8gd29ybGQ.tunnel.example.com"))

Key detection indicators:

1. High Shannon entropy in query names (> 3.5 for subdomain labels)
2. Unusually long query names (> 50 characters)
3. High volume of TXT record requests to a single domain
4. High unique subdomain count per parent domain
5. Non-standard character distribution in labels

Examples

from scapy.all import rdpcap, DNS, DNSQR
packets = rdpcap()
 pkt  packets:
     pkt.haslayer(DNSQR):
        query = pkt[DNSQR].qname.decode()
        entropy = shannon_entropy(query)
         entropy > :
            ()