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.

仓库: autohandai/community-skills
最近来源活动: 2026年3月16日 00:59
检测到的 SKILL.md 语言: 英语
星标: 10
分支: 3

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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

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

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

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

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

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

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

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

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

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

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

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