
analyzing-dns-logs-for-exfiltration
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-dns-logs-for-exfiltration- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads analyzing-dns-logs-for-exfiltration automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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,dnssourcetype, 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
mathandcollectionslibraries 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, suspicion
Step 4: Detect Known DNS Tunneling Tools
Search for signatures of common DNS tunneling tools:
index=dns sourcetype="stream:dns"
| eval query_lower = lower(query)
| where (
match(query_lower, "\.dnscat\.") OR
match(query_lower, "\.dns2tcp\.") OR
match(query_lower, "\.iodine\.") OR
match(query_lower, "\.dnscapy\.") OR
match(query_lower, "\.cobalt.*\.beacon") OR
query_type="NULL" OR
(query_type="TXT" AND len(query) > 100)
)
| stats count by src_ip, query, query_type
| sort - count
Detect DNS over HTTPS (DoH) bypassing local DNS:
index=proxy OR index=firewall
dest IN ("1.1.1.1", "1.0.0.1", "8.8.8.8", "8.8.4.4",
"9.9.9.9", "149.112.112.112", "208.67.222.222")
dest_port=443
| stats sum(bytes_out) AS total_bytes, count AS connections by src_ip, dest
| where connections > 100 OR total_bytes > 10485760
| eval alert = "Possible DoH bypass — DNS queries sent over HTTPS to public resolver"
| sort - total_bytes
Step 5: Correlate DNS Findings with Endpoint Data
Cross-reference suspicious DNS with process data:
index=dns src_ip="192.168.1.105" query="*.evil-tunnel.com" earliest=-24h
| stats count AS dns_queries, earliest(_time) AS first_query, latest(_time) AS last_query
by src_ip, query
| join src_ip [
search index=sysmon EventCode=3 DestinationPort=53 Computer="WORKSTATION-042"
| stats count AS connections, values(Image) AS processes by SourceIp
| rename SourceIp AS src_ip
]
| table src_ip, query, dns_queries, first_query, last_query, processes
Step 6: Calculate Data Exfiltration Volume Estimate
Estimate data volume encoded in DNS queries:
index=dns src_ip="192.168.1.105" query="*.evil-tunnel.com" earliest=-24h
| eval domain_parts = split(query, ".")
| eval encoded_data = mvindex(domain_parts, 0)
| eval encoded_bytes = len(encoded_data)
| eval decoded_bytes = encoded_bytes * 0.75 -- Base64 decoding factor
| stats sum(decoded_bytes) AS total_bytes_estimated, count AS total_queries,
earliest(_time) AS first_seen, latest(_time) AS last_seen
| eval estimated_kb = round(total_bytes_estimated / 1024, 1)
| eval estimated_mb = round(total_bytes_estimated / 1048576, 2)
| eval duration_hours = round((last_seen - first_seen) / 3600, 1)
| eval rate_kbps = round(estimated_kb / (duration_hours * 3600) * 8, 2)
| table total_queries, estimated_mb, duration_hours, rate_kbps, first_seen, last_seen
Key Concepts
| Term | Definition |
|---|---|
| DNS Tunneling | Technique encoding data within DNS queries/responses to exfiltrate data or establish C2 channels through DNS |
| DGA | Domain Generation Algorithm — malware technique generating pseudo-random domain names for C2 resilience |
| Shannon Entropy | Mathematical measure of randomness in a string — high entropy (>3.5) in domain names indicates DGA or tunneling |
| TXT Record Abuse | Using DNS TXT records (designed for text data) as a high-bandwidth channel for data tunneling |
| DNS over HTTPS (DoH) | DNS queries encrypted over HTTPS (port 443), bypassing traditional DNS monitoring |
| Passive DNS | Historical record of DNS resolutions showing which IPs a domain resolved to over time |
Tools & Systems
- Splunk Stream: Network traffic capture add-on providing parsed DNS query data for SIEM analysis
- Zeek (Bro): Network security monitor generating detailed DNS transaction logs for analysis
- Cisco Umbrella (OpenDNS): Cloud DNS security platform blocking malicious domains and logging query data
- Infoblox DNS Firewall: DNS-layer security providing RPZ-based blocking and detailed query logging
- Farsight DNSDB: Passive DNS database for historical domain resolution lookups and infrastructure mapping
Common Scenarios
- Cobalt Strike DNS Beacon: Detect periodic TXT queries with encoded payloads to C2 domain
- Data Exfiltration: Large volumes of unique subdomain queries encoding stolen data in Base64/hex
- DGA Malware: Detect DNS queries to algorithmically generated domains (high entropy, no web content)
- DNS-over-HTTPS Bypass: Employee using DoH to bypass corporate DNS filtering and monitoring
- Slow Drip Exfiltration: Low-volume DNS tunneling staying below threshold alerts (requires baseline comparison)
Output Format
DNS EXFILTRATION ANALYSIS — WORKSTATION-042
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Period: 2024-03-14 to 2024-03-15
Source: 192.168.1.105 (WORKSTATION-042, Finance Dept)
Findings:
[CRITICAL] DNS tunneling detected to evil-tunnel[.]com
Query Volume: 12,847 queries in 18 hours
Avg Subdomain Len: 63 characters (normal: <20)
Avg Entropy: 3.82 (threshold: 3.5)
Query Types: TXT (89%), A (11%)
Estimated Data: ~4.7 MB exfiltrated via DNS
Rate: 0.58 kbps (slow drip pattern)
[HIGH] DGA-like domains resolved
Unique DGA Domains: 247 domains resolved
Pattern: 15-char random alphanumeric.xyz TLD
Entropy Range: 3.6 - 4.1
Process Attribution:
Process: svchost_update.exe (masquerading — not legitimate svchost)
PID: 4892
Parent: explorer.exe
Hash: SHA256: a1b2c3d4... (VT: 34/72 malicious — Cobalt Strike beacon)
Containment:
[DONE] Host isolated via EDR
[DONE] Domain evil-tunnel[.]com added to DNS sinkhole
[DONE] Incident IR-2024-0448 created
FAQ
What does the analyzing-dns-logs-for-exfiltration skill do?
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.
How do I install analyzing-dns-logs-for-exfiltration?
Run npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-dns-logs-for-exfiltration in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/analyzing-dns-logs-for-exfiltration/ (personal) or .claude/skills/analyzing-dns-logs-for-exfiltration/ (project).
Which AI agents can use analyzing-dns-logs-for-exfiltration?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is analyzing-dns-logs-for-exfiltration safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains analyzing-dns-logs-for-exfiltration?
It is published by mukul975 in mukul975/anthropic-cybersecurity-skills. It has 529 recorded installs.
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