analyzing-dns-logs-for-exfiltrationlisted

# 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(d