analyzing-dns-logs-for-exfiltration

Files (4)

• LICENSE
• SKILL.md
• api-reference.md · references/
• agent.py · scripts/

SKILL.md (first lines)

# 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:

```spl
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:

```spl
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:**

```python
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:**

```spl
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:

```spl
index=dns sourcetype="stream:dns" earliest=-24h

From Joyooosama/dsh-skills-for-forensics, Apache-2.0.

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