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.
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.
Python-based Shannon Entropy Calculation for DNS queries:
import math
from collections import Counter
defshannon_entropy(text):
"""Calculate Shannon entropy of a string"""ifnot text:
return0
counter = Counter(text.lower())
length = len(text)
entropy = -sum(
(count / length) * math.log2(count / length)
for count in counter.values()
)
returnround(entropy, 4)
# Test with examples
normal_domain = "google"# Low entropy
dga_domain = "x8kj2m9p4qw7n"# High entropy
tunnel_subdomain = "aGVsbG8gd29ybGQ.evil.com"# Base64 encoded dataprint(f"Normal: {shannon_entropy(normal_domain)}") # ~2.25print(f"DGA: {shannon_entropy(dga_domain)}") # ~3.70print(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
