  Active measurements can be used to collect server characteristics on a large
scale. This kind of metadata can help discovering hidden relations and
commonalities among server deployments offering new possibilities to cluster
and classify them. As an example, identifying a previously-unknown
cybercriminal infrastructures can be a valuable source for cyber-threat
intelligence. We propose herein an active measurement-based methodology for
acquiring Transport Layer Security (TLS) metadata from servers and leverage it
for their fingerprinting. Our fingerprints capture the characteristic behavior
of the TLS stack primarily caused by the implementation, configuration, and
hardware support of the underlying server. Using an empirical optimization
strategy that maximizes information gain from every handshake to minimize
measurement costs, we generated 10 general-purpose Client Hellos used as
scanning probes to create a large database of TLS configurations used for
classifying servers. We fingerprinted 28 million servers from the Alexa and
Majestic toplists and two Command and Control (C2) blocklists over a period of
30 weeks with weekly snapshots as foundation for two long-term case studies:
classification of Content Delivery Network and C2 servers. The proposed
methodology shows a precision of more than 99 % and enables a stable
identification of new servers over time. This study describes a new opportunity
for active measurements to provide valuable insights into the Internet that can
be used in security-relevant use cases.
