AI Cyber Defense / Network detection, DNS and egress analytics
AI Cyber Defense: Investigate DNS tunneling hypotheses with multiple signals
Combine DNS shape with volume, destination novelty and endpoint context. Use resolver enforcement and incident review before blocking a shared provider domain.
Environment and inputs: Network detection and response using resolver logs, proxy metadata, flow records and optional Zeek or Suricata sensors.
Scenario and goal
Long or unusual DNS labels can arise from legitimate services as well as covert data transfer.
Why it matters
For this scenario, expect behavior to vary across destinations, timing and client software. Network metadata supports a hypothesis, while endpoint and identity records often explain the actor and purpose. Account for encryption, sampling and sensor placement before interpreting absence. A statistical anomaly, protocol fingerprint or unfamiliar domain should remain a supporting observation until its consequence and business context are established.
Evidence to collect
Collect query length, label diversity, query type, response code, source host and parent-domain behavior over a defined window.
Validate with an authorized exercise
Compare synthetic benign telemetry names with a lab-only anomalous query series; confirm the alert records why each case was included or excluded.
Rollout and checks
Preserve event time, source tuple, sensor identity and the original record identifier for every decisive observation. Verify clock alignment and collection health before joining sources. Compare a representative benign workload with the suspected behavior in a lab or stored-event replay. Adjust a detection only after documenting which observed distinction justifies the change.
Scope and limits
This guide proposes a defensive test; it is not a tested production design. Check platform support and local requirements. References explain the mechanisms but do not certify the design. Behavior alone does not prove AI involvement.