I am a creative mind passionate about designing and implementing ideas.
As software engineer I am interested in software development, software architectures, and large computer networks.
As PhD Student I can combine these interest in my research where I try to find ways to model the architecture of the whole Internet.
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Jul 29, 2022
Invited to present my TMA research paper at the IETF 114.
A recording is available on [YouTube].
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,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,}
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,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},doi={10.1007/978-3-031-28486-1_6},}
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,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},doi={10.1145/3624354.3630585}}
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