Federated Graph Queries Across DNS and WHOIS Big‑Data Stores

In the realm of cyber threat intelligence, few data sources are as fundamental yet underutilized in combination as DNS logs and WHOIS records. DNS data captures the dynamic, often real-time behavior of domain resolutions across networks, while WHOIS data encodes the more static, registrant-level metadata about domain ownership and administrative control. When analyzed in isolation, each offers a valuable but incomplete view. The true analytical power emerges when these datasets are combined, enabling analysts to trace relationships between domains, IP addresses, registrants, name servers, and organizations. The challenge lies in the nature and scale of these datasets, which are both massive and structurally diverse. Federated graph queries provide a powerful mechanism to bridge these two domains of knowledge at big-data scale, supporting complex reasoning across distributed, heterogeneous data stores.

Graph representations are particularly well-suited for this task. In a graph model, domains, IPs, WHOIS entities, and DNS resource records are modeled as nodes, with edges denoting relationships such as resolution events, ownership, shared infrastructure, or registration overlaps. For example, an edge might connect a domain to an IP it resolved to, a domain to a registrant email, or a nameserver to multiple domains it hosts. These interconnections form a rich web of relationships that are critical in uncovering infrastructure reuse, botnet footprints, phishing campaigns, and evasive tactics like fast-flux or DGA domain clustering.

However, the sheer size and disparate formats of DNS and WHOIS datasets complicate joint analysis. DNS data, especially when collected passively at scale, generates billions of records daily, consisting of query logs, resolved IPs, response codes, TTLs, and client metadata. This data typically lives in streaming or partitioned storage frameworks like Apache Kafka, Delta Lake, or cloud-based data lakes. WHOIS data, on the other hand, is slower to change but no less voluminous, especially when collected globally from registrars and registries. It is often semi-structured, stored in JSON, XML, or flat text, and includes fields such as domain creation dates, registrant names, addresses, emails, and abuse contacts.

Federated graph query architectures allow analysts to query across these disparate stores without needing to merge or centralize the data physically. Instead, the query engine operates over a virtualized schema that spans multiple backends, each optimized for different workloads. For example, DNS data may be stored in a distributed columnar format like Apache Iceberg for high-speed scans, while WHOIS data may reside in a document store or graph database like Neo4j, Amazon Neptune, or TigerGraph. The federated engine, which may be built using tools like Apache AGE, Gremlin with federation extensions, or custom GraphQL over data virtualization layers, compiles queries into subcomponents that are dispatched to the appropriate backend systems and joined at runtime.

This approach enables powerful investigations. Consider an analyst seeking to identify all domains that have resolved to a given set of IPs in the last 24 hours, and then determine which of those domains are registered by entities linked to known malicious actors. The initial portion of the query targets DNS logs, possibly filtered via windowed time constraints and IP match conditions, using parallelized scans to isolate relevant domain names. The second portion uses those domain names to look up WHOIS registrant emails or organization fields and applies fuzzy matching or exact correlation against known bad actor lists. With federated querying, the results from each data source can be merged into a cohesive graph path, even though the underlying data lives in separate systems and formats.

Another use case is clustering infrastructure based on behavioral and ownership similarity. Domains that resolve to the same IP range, are registered using the same WHOIS email, or share the same TTL and nameserver configurations may be part of the same campaign. By issuing federated queries that traverse multiple graph layers—DNS activity, WHOIS ownership, registrar history—analysts can identify previously unknown nodes in malicious infrastructure. These insights can then be used to populate DNS response policy zones, feed threat intelligence databases, or generate signatures for intrusion detection systems.

Security teams also benefit from federated graphs during incident response. When investigating a DNS beaconing event from an endpoint, they can trace the resolving domain back to WHOIS records to determine whether the domain is freshly registered, privacy protected, or linked to known abuse patterns. They can simultaneously investigate whether the resolving IP has hosted multiple suspicious domains or whether it appears in reverse DNS lookups tied to bulletproof hosting providers. With federated graph queries, this entire chain of reasoning can be executed in a single logical query, allowing analysts to pivot quickly and automatically correlate across datasets that would otherwise require separate tools and manual data stitching.

Performance optimization in federated query systems is non-trivial. DNS logs are highly time-series in nature and benefit from partition pruning by date or source. WHOIS data, conversely, benefits from indexing on text fields like emails, names, and registrant IDs. Efficient federation must ensure that each query component leverages the native performance features of its respective backend. This requires smart query planners, statistics on data cardinality, and caching mechanisms to avoid redundant lookups. Some implementations use materialized subgraphs for frequently joined nodes—such as known malware infrastructure or registrar abuse clusters—so that these queries can be answered quickly without re-executing the full federated path.

Access control and data governance are additional concerns. WHOIS data often contains personally identifiable information (PII) and must be handled in accordance with privacy regulations such as GDPR. Federated graph platforms must support fine-grained access policies, masking sensitive attributes or restricting query results based on user roles. For example, a SOC analyst may be permitted to see all DNS-to-IP relationships, but only de-identified WHOIS registrant data. These policies must be enforced across all federated backends, requiring integration with authentication and authorization systems like LDAP, OAuth, or attribute-based access control engines.

As cyber threats grow more modular and distributed, the need to perform integrated analysis across infrastructure data becomes paramount. Federated graph querying across DNS and WHOIS big-data stores provides a scalable, flexible, and analytically powerful approach to navigating the interconnected landscape of internet infrastructure. It enables security operations, research, and threat intelligence teams to move beyond siloed views and toward a unified graph of relationships that reflects both the dynamic activity of DNS and the structural ownership context of WHOIS. In doing so, it transforms the act of querying into a form of real-time reasoning—drawing connections across billions of records to expose the hidden infrastructure behind modern cyber threats.

In the realm of cyber threat intelligence, few data sources are as fundamental yet underutilized in combination as DNS logs and WHOIS records. DNS data captures the dynamic, often real-time behavior of domain resolutions across networks, while WHOIS data encodes the more static, registrant-level metadata about domain ownership and administrative control. When analyzed in isolation,…