Active TLS Stack Fingerprinting: Characterizing TLS Server Deployments at Scale

TL;DR

This paper presents an active measurement methodology for fingerprinting TLS servers at scale, enabling high-precision classification and long-term tracking of server deployments for security insights.

Contribution

It introduces a novel active measurement approach that captures TLS stack characteristics for large-scale server fingerprinting and classification.

Findings

Fingerprinting 28 million servers with over 99% precision

Effective long-term tracking of server changes over 30 weeks

Identification of server types like CDN and C2 servers

Abstract

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.