Imported from mukul975/anthropic-cybersecurity-skills (
skills/performing-dns-tunneling-detection/SKILL.md). Install upstream withnpx skills add mukul975/anthropic-cybersecurity-skills --skill performing-dns-tunneling-detection. Copyright stays with the author (Apache-2.0).
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:
- High Shannon entropy in query names (> 3.5 for subdomain labels)
- Unusually long query names (> 50 characters)
- High volume of TXT record requests to a single domain
- High unique subdomain count per parent domain
- 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})")