publications | Markus Sosnowski

2024

IEEE TNSM
EFACTLS: Effective Active TLS Fingerprinting for Large-scale Server Deployment Characterization

Markus Sosnowski, Johannes Zirngibl, Patrick Sattler, and 4 more authors

IEEE Transactions on Network and Service Management, Feb 2024

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Active measurements allow the collection of server characteristics on a large scale that can aid in discovering hidden relations and commonalities among server deployments. Finding these relations opens up new possibilities for clustering and classifying server deployments; for example, identifying a previously unknown cybercriminal infrastructure can be valuable cyber-threat intelligence. In this work, we propose a methodology based on active measurements to acquire Transport Layer Security (TLS) metadata from servers and leverage it for fingerprinting. Our fingerprints capture characteristic behavior of the TLS stack, primarily influenced by the server’s implementation, configuration, and hardware support. Using an empirical optimization strategy that maximizes information gained from every handshake to minimize measurement costs, we generated 10 general-purpose Client Hellos. They served as scanning probes to create an extensive database of TLS configurations to classify servers. We propose the Shannon Entropy to measure collected information and compare different approaches. This study fingerprinted 8 million servers from the Tranco top list and two Command and Control (C2) blocklists over 60 weeks with weekly snapshots. The resulting data formed the foundation for two long-term case studies: classification of Content Delivery Network and C2 servers. Moreover, the detection was fine-grained enough to detect C2 server families. The proposed methodology demonstrated a precision of 99% and enabled a stable identification of new servers over time. This study shows how active measurements can provide valuable security-relevant insights and improve our understanding of the Internet.
@article{sosnowski2024efactls, bibtex_show = {true}, abbr = {IEEE TNSM}, author = {Sosnowski, Markus and Zirngibl, Johannes and Sattler, Patrick and Carle, Georg and Grohnfeldt, Claas and Russo, Michele and Sgandurra, Daniele}, journal = {{IEEE Transactions on Network and Service Management}}, title = {{EFACTLS: Effective Active TLS Fingerprinting for Large-scale Server Deployment Characterization}}, year = {2024}, month = feb, keywords = {Servers;Fingerprint recognition;Protocols;Behavioral sciences;Probes;Internet;Feature extraction;Active Scanning;TLS;Fingerprinting;Server Classification;Command and Control Servers}, doi = {10.1109/TNSM.2024.3364526}, website = {https://tumi8.github.io/active-tls-fingerprinting/}, selected = true, pdf = {https://doi.org/10.1109/TNSM.2024.3364526} }

2023

CoNEXT23
The Performance of Post-Quantum TLS 1.3

Markus Sosnowski, Florian Wiedner, Eric Hauser, and 4 more authors

In Proc. International Conference on emerging Networking EXperiments and Technologies (CoNEXT), Dec 2023

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Quantum Computers (QCs) differ radically from traditional computers and can efficiently solve mathematical problems fundamental to our current cryptographic algorithms. Although existing QCs need to accommodate more qubits to break cryptographic algorithms, the concern of "Store-Now-Decrypt-Later" (i.e., adversaries store encrypted data today and decrypt them once powerful QCs become available) highlights the necessity to adopt quantum-safe approaches as soon as possible. In this work, we investigate the performance impact of Post-Quantum Cryptography (PQC) on TLS 1.3. Different signature algorithms and key agreements (as proposed by the National Institute of Standards and Technology (NIST)) are examined through black- and white-box measurements to get precise handshake latencies and computational costs per participating library. We emulated loss, bandwidth, and delay to analyze constrained environments. Our results reveal that HQC and Kyber are on par with our current state-of-the-art, while Dilithium and Falcon are even faster. We observed no performance drawback from using hybrid algorithms; moreover, on higher NIST security levels, PQC outperformed any algorithm in use today. Hence, we conclude that post-quantum TLS is suitable for adoption in today’s systems.
@inproceedings{10.1145/3624354.3630585, bibtex_show = {true}, abbr = {CoNEXT23}, author = {Sosnowski, Markus and Wiedner, Florian and Hauser, Eric and Steger, Lion and Schoinianakis, Dimitrios and Gallenm{\"u}ller, Sebastian and Carle, Georg}, title = {{The Performance of Post-Quantum TLS 1.3}}, booktitle = {Proc. International Conference on emerging Networking EXperiments and Technologies (CoNEXT)}, year = {2023}, address = {Paris, France}, month = dec, keywords = {performance measurements, post-quantum cryptography}, website = {https://tumi8.github.io/pqs-tls-measurements}, doi = {10.1145/3624354.3630585}, pdf = {https://dl.acm.org/doi/10.1145/3624354.3630585?cid=99659319027}, selected = {true} }
PAM23
DissecTLS: A Scalable Active Scanner for TLS Server Configurations, Capabilities, and TLS Fingerprinting

Markus Sosnowski, Johannes Zirngibl, Patrick Sattler, and 1 more author

In Proc. Passive and Active Measurement (PAM), Dec 2023

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Collecting metadata from Transport Layer Security (TLS) servers on a large scale allows to draw conclusions about their capabilities and configuration. This provides not only insights into the Internet but it enables use cases like detecting malicious Command and Control (C &C) servers. However, active scanners can only observe and interpret the behavior of TLS servers, the underlying configuration and implementation causing the behavior remains hidden. Existing approaches struggle between resource intensive scans that can reconstruct this data and light-weight fingerprinting approaches that aim to differentiate servers without making any assumptions about their inner working. With this work we propose DissecTLS, an active TLS scanner that is both light-weight enough to be used for Internet measurements and able to reconstruct the configuration and capabilities of the TLS stack. This was achieved by modeling the parameters of the TLS stack and derive an active scan that dynamically creates scanning probes based on the model and the previous responses from the server. We provide a comparison of five active TLS scanning and fingerprinting approaches in a local testbed and on toplist targets. We conducted a measurement study over nine weeks to fingerprint C &C servers and analyzed popular and deprecated TLS parameter usage. Similar to related work, the fingerprinting achieved a maximum precision of 99 % for a conservative detection threshold of 100 %; and at the same time, we improved the recall by a factor of 2.8.
@inproceedings{10.1007/978-3-031-28486-1_6, bibtex_show = {true}, abbr = {PAM23}, author = {Sosnowski, Markus and Zirngibl, Johannes and Sattler, Patrick and Carle, Georg}, editor = {Brunstrom, Anna and Flores, Marcel and Fiore, Marco}, title = {{DissecTLS: A Scalable Active Scanner for TLS Server Configurations, Capabilities, and TLS Fingerprinting}}, booktitle = {Proc. Passive and Active Measurement (PAM)}, year = {2023}, publisher = {Springer Nature Switzerland}, pages = {110--126}, isbn = {978-3-031-28486-1}, website = {https://dissectls.github.io/}, doi = {10.1007/978-3-031-28486-1_6}, pdf = {https://link.springer.com/content/pdf/10.1007/978-3-031-28486-1_6.pdf?pdf=inline%20link}, selected = {true} }

2022

TMA22
Active TLS Stack Fingerprinting: Characterizing TLS Server Deployments at Scale

Markus Sosnowski, Johannes Zirngibl, Patrick Sattler, and 4 more authors

In Proc. Network Traffic Measurement and Analysis Conference (TMA), Jun 2022

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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.
@inproceedings{sosnowski2022tlsfingerprinting, bibtex_show = {true}, best_paper = {true}, abbr = {TMA22}, author = {Sosnowski, Markus and Zirngibl, Johannes and Sattler, Patrick and Carle, Georg and Grohnfeldt, Claas and Russo, Michele and Sgandurra, Daniele}, title = {{Active TLS Stack Fingerprinting: Characterizing TLS Server Deployments at Scale}}, booktitle = {Proc. Network Traffic Measurement and Analysis Conference (TMA)}, year = {2022}, month = jun, website = {https://active-tls-fingerprinting.github.io/}, pdf = {https://tma.ifip.org/2022/wp-content/uploads/sites/11/2022/06/tma2022-paper35.pdf}, selected = {true} }