/analyzing-dns-logs-for-exfiltration

name: analyzing-dns-logs-for-exfiltration
description: 'Analyzes DNS query logs to detect data exfiltration via DNS tunneling,
  DGA domain communication, and covert C2 channels using entropy analysis, query volume
  anomalies, and subdomain length detection in SIEM platforms. Use when SOC teams
  need to identify DNS-based threats that bypass traditional network security controls.

  '
domain: cybersecurity
subdomain: soc-operations
tags:
- soc
- dns
- exfiltration
- dns-tunneling
- dga
- c2-detection
- splunk
- threat-detection
version: '1.0'
author: mahipal
license: Apache-2.0
atlas_techniques:
- AML.T0024
- AML.T0056
- AML.T0086
nist_csf:
- DE.CM-01
- DE.AE-02
- RS.MA-01
- DE.AE-06
mitre_attack:
- T1048.003
- T1071.004
- T1567

Analyzing DNS Logs for Exfiltration

When to Use

Use this skill when:

• SOC teams suspect data exfiltration through DNS tunneling to bypass firewall/proxy controls
• Threat intelligence indicates adversaries using DNS-based C2 channels (e.g., Cobalt Strike DNS beacon)
• UEBA detects anomalous DNS query volumes from specific hosts
• Malware analysis reveals DNS-over-HTTPS (DoH) or DNS tunneling capabilities

**Do not use** for standard DNS troubleshooting or availability monitoring — this skill focuses on security-relevant DNS abuse detection.

Prerequisites

• DNS query logging enabled (Windows DNS Server, Bind, Infoblox, or Cisco Umbrella)
• DNS logs ingested into SIEM (Splunk with `Stream:DNS`, `dns` sourcetype, or Zeek DNS logs)
• Passive DNS data for historical domain resolution analysis
• Baseline of normal DNS behavior (query volume, domain distribution, TXT record frequency)
• Python with `math` and `collections` libraries for entropy calculation

Workflow

Step 1: Detect DNS Tunneling via Subdomain Length Analysis

DNS tunneling encodes data in subdomain labels, creating unusually long queries:

index=dns sourcetype="stream:dns" query_type IN ("A", "AAAA", "TXT", "CNAME", "MX")
| eval domain_parts = split(query, ".")
| eval subdomain = mvindex(domain_parts, 0, mvcount(domain_parts)-3)
| eval subdomain_str = mvjoin(subdomain, ".")
| eval subdomain_len = len(subdomain_str)
| eval tld = mvindex(domain_parts, -1)
| eval registered_domain = mvindex(domain_parts, -2).".".tld
| where subdomain_len > 50
| stats count AS queries, dc(query) AS unique_queries,
        avg(subdomain_len) AS avg_subdomain_len,
        max(subdomain_len) AS max_subdomain_len,
        values(src_ip) AS sources
  by registered_domain
| where queries > 20
| sort - avg_subdomain_len
| table registered_domain, queries, unique_queries, avg_subdomain_len, max_subdomain_len, sources

Step 2: Detect High-Entropy Domain Queries (DGA Detection)

Domain Generation Algorithms produce random-looking domains:

index=dns sourcetype="stream:dns"
| eval domain_parts = split(query, ".")
| eval sld = mvindex(domain_parts, -2)
| eval sld_len = len(sld)
| eval char_count = sld_len
| eval vowels = len(replace(sld, "[^aeiou]", ""))
| eval consonants = len(replace(sld, "[^bcdfghjklmnpqrstvwxyz]", ""))
| eval digits = len(replace(sld, "[^0-9]", ""))
| eval vowel_ratio = if(char_count > 0, vowels / char_count, 0)
| eval digit_ratio = if(char_count > 0, digits / char_count, 0)
| where sld_len > 12 AND (vowel_ratio < 0.2 OR digit_ratio > 0.3)
| stats count AS queries, dc(query) AS unique_domains, values(src_ip) AS sources
  by query
| where unique_domains > 10
| sort - queries

**Python-based Shannon Entropy Calculation for DNS queries:**

import math
from collections import Counter

def shannon_entropy(text):
    """Calculate Shannon entropy of a string"""
    if not text:
        return 0
    counter = Counter(text.lower())
    length = len(text)
    entropy = -sum(
        (count / length) * math.log2(count / length)
        for count in counter.values()
    )
    return round(entropy, 4)

# Test with examples
normal_domain = "google"           # Low entropy
dga_domain = "x8kj2m9p4qw7n"      # High entropy
tunnel_subdomain = "aGVsbG8gd29ybGQ.evil.com"  # Base64 encoded data

print(f"Normal: {shannon_entropy(normal_domain)}")     # ~2.25
print(f"DGA:    {shannon_entropy(dga_domain)}")         # ~3.70
print(f"Tunnel: {shannon_entropy(tunnel_subdomain)}")   # ~3.50

# Threshold: entropy > 3.5 for subdomain = likely tunneling/DGA

**Splunk implementation of entropy scoring:**

index=dns sourcetype="stream:dns"
| eval domain_parts = split(query, ".")
| eval check_string = mvindex(domain_parts, 0)
| eval check_len = len(check_string)
| where check_len > 8
| eval chars = split(check_string, "")
| stats count AS total_chars, dc(chars) AS unique_chars by query, src_ip, check_string, check_len
| eval entropy_estimate = log(unique_chars, 2) * (unique_chars / check_len)
| where entropy_estimate > 3.5
| stats count AS high_entropy_queries, dc(query) AS unique_queries by src_ip
| where high_entropy_queries > 50
| sort - high_entropy_queries

Step 3: Detect Anomalous DNS Query Volume

Identify hosts generating abnormal DNS traffic:

index=dns sourcetype="stream:dns" earliest=-24h
| bin _time span=1h
| stats count AS queries, dc(query) AS unique_domains by src_ip, _time
| eventstats avg(queries) AS avg_queries, stdev(queries) AS stdev_queries by src_ip
| eval z_score = (queries - avg_queries) / stdev_queries
| where z_score > 3 OR queries > 5000
| sort - z_score
| table _time, src_ip, queries, unique_domains, avg_queries, z_score

**Detect TXT record abuse (common tunneling method):**

index=dns sourcetype="stream:dns" query_type="TXT"
| stats count AS txt_queries, dc(query) AS unique_txt_domains,
        values(query) AS domains by src_ip
| where txt_queries > 100
| eval suspicion = case(
    txt_queries > 1000, "CRITICAL — Likely DNS tunneling",
    txt_queries > 500, "HIGH — Possible DNS tunneling",
    txt_queries > 100, "MEDIUM — Unusual TXT volume"
  )
| sort - txt_queries
| table src_ip, txt_queries, unique_txt_domains, su