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

---
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: [performing, dns, tunneling, detection]
version: "1.0"
author: mahipal
license: MIT
---

# Performing DNS Tunneling Detection

## Instructions

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

```python
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

```python
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})")
```