Anthropic-Cybersecurity-Skills/skills/performing-dns-tunneling-detection/SKILL.md

name, description, domain, subdomain, tags, version, author, license, nist_csf

name	description	domain	subdomain	tags	version	author	license	nist_csf
performing-dns-tunneling-detection	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.	cybersecurity	security-operations	performing dns tunneling detection	1.0	mahipal	Apache-2.0	DE.CM-01 RS.MA-01 GV.OV-01 DE.AE-02

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("dns_traffic.pcap")
for pkt in packets:
    if pkt.haslayer(DNSQR):
        query = pkt[DNSQR].qname.decode()
        entropy = shannon_entropy(query)
        if entropy > 4.0:
            print(f"Suspicious: {query} (entropy={entropy:.2f})")