seikaikyo/hunting-for-dns-tunneling-with-zeek

Hunting for DNS Tunneling with Zeek

When to Use

• When hunting for data exfiltration over DNS covert channels
• After threat intelligence indicates DNS-based C2 frameworks targeting your industry
• When dns.log shows unusually high query volumes to specific domains
• During investigation of suspected data theft where no HTTP/S exfiltration is found
• When monitoring for tools like iodine, dnscat2, DNSExfiltrator, or DNS-over-HTTPS tunneling

Prerequisites

• Zeek deployed on network tap or SPAN port capturing DNS traffic
• Zeek dns.log with full query and response fields
• SIEM platform for dns.log analysis (Splunk, Elastic)
• RITA (Real Intelligence Threat Analytics) for automated DNS analysis
• Passive DNS data for historical domain resolution context

Workflow

1. Analyze Query Length Distribution: DNS tunneling encodes data in subdomain labels, producing queries significantly longer than normal. Normal DNS queries average 20-30 characters; tunneling queries often exceed 50+ characters. Calculate mean and standard deviation of query lengths per domain.
2. Calculate Subdomain Entropy: Tunneling encodes data using Base32/Base64, producing high-entropy subdomain strings. Calculate Shannon entropy of subdomain labels -- values above 3.5 bits/character strongly suggest encoded data.
3. Count Unique Subdomains Per Domain: Legitimate domains have relatively few unique subdomains. DNS tunneling generates hundreds or thousands of unique subdomains under a single parent domain.
4. Monitor DNS Record Type Distribution: TXT, NULL, CNAME, and MX records can carry more data than A records. Excessive TXT queries to a single domain indicate data transfer via DNS.
5. Detect High Query Volume: Flag domains receiving more than 100 queries per hour from a single source, especially when combined with high subdomain uniqueness.
6. Analyze Query Timing: DNS tunneling tools produce regular query patterns (beaconing) or burst patterns (data transfer). Apply frequency analysis to DNS query timestamps.
7. Cross-Reference with conn.log: Correlate DNS queries with connection metadata to identify the process or endpoint generating suspicious queries.
8. Validate with Domain Intelligence: Check suspicious domains against WHOIS data, certificate transparency, and threat intelligence feeds.

Key Concepts

| Concept | Description | |---------|-------------| | T1071.004 | Application Layer Protocol: DNS | | T1048.003 | Exfiltration Over Alternative Protocol: DNS | | T1572 | Protocol Tunneling | | Shannon Entropy | Measure of randomness in subdomain strings | | Zeek dns.log | DNS query/response metadata | | RITA | Automated DNS tunneling detection from Zeek logs | | iodine | IPv4-over-DNS tunneling tool | | dnscat2 | DNS-based command-and-control tool | | DNSExfiltrator | Data exfiltration tool using DNS requests |

Detection Queries

Zeek Script -- DNS Tunnel Detection

@load base/protocols/dns
module DNSTunnel;

export {
    redef enum Notice::Type += { DNSTunnel::Long_DNS_Query };
    const query_length_threshold = 50 &redef;
    const query_count_threshold = 100 &redef;
}

event dns_request(c: connection, msg: dns_msg, query: string, qtype: count, qclass: count) {
    if ( |query| > query_length_threshold ) {
        NOTICE([$note=DNSTunnel::Long_DNS_Query,
                $msg=fmt("Long DNS query detected: %s (%d chars)", query, |query|),
                $conn=c]);
    }
}

Splunk -- DNS Tunneling Indicators from Zeek

index=zeek sourcetype=bro_dns
| rex field=query "(?<subdomain>[^.]+)\.(?<basedomain>[^.]+\.[^.]+)$"
| stats count dc(subdomain) as unique_subs avg(len(query)) as avg_len max(len(query)) as max_len by src basedomain
| where count > 100 AND (unique_subs > 50 OR avg_len > 40)
| sort -unique_subs

Splunk -- High Entropy Subdomain Detection

index=zeek sourcetype=bro_dns
| rex field=query "^(?<subdomain>[^.]+)"
| where len(subdomain) > 20
| eval char_count=len(subdomain)
| stats count dc(query) as unique_queries avg(char_count) as avg_sub_len by src query_type_name basedomain
| where unique_queries > 30 AND avg_sub_len > 25
| sort -unique_queries

RITA Analysis

rita import /path/to/zeek/logs dataset_name
rita show-dns-fqdn-ips-long dataset_name
rita show-exploded-dns dataset_name
rita show-dns-tunneling dataset_name --csv > dns_tunnel_results.csv

Common Scenarios

1. dnscat2 C2: Encodes command-and-control traffic in DNS CNAME/TXT queries with Base64-encoded subdomain labels. Produces high query volumes with long, high-entropy subdomains.
2. iodine IPv4 Tunnel: Creates a virtual network interface tunneling all IP traffic through DNS. Generates massive DNS query volumes with NULL record types.
3. Data Exfiltration via DNS: Sensitive data encoded in subdomain labels (e.g., aGVsbG8gd29ybGQ.exfil.attacker.com), sent as A or TXT queries. Each query carries ~63 bytes of data.
4. DNS-over-HTTPS Tunneling: Bypasses traditional DNS monitoring by sending DNS queries over HTTPS to public resolvers (8.8.8.8, 1.1.1.1), requiring TLS inspection for detection.
5. Cobalt Strike DNS Beacon: Uses DNS A/TXT records for C2 communication with configurable subdomain encoding schemes.

Output Format

Hunt ID: TH-DNSTUNNEL-[DATE]-[SEQ]
Source IP: [Internal IP]
Source Host: [Hostname]
Target Domain: [Base domain]
Query Count: [Total queries in window]
Unique Subdomains: [Count]
Avg Query Length: [Characters]
Max Query Length: [Characters]
Subdomain Entropy: [Bits per character]
Primary Record Type: [A/TXT/CNAME/NULL]
Data Volume Estimate: [Bytes exfiltrated]
Risk Level: [Critical/High/Medium/Low]