Paul Fiterau-Brostean (Uppsala University, Sweden), Bengt Jonsson (Uppsala University, Sweden), Konstantinos Sagonas (Uppsala University, Sweden and National Technical University of Athens, Greece), Fredrik Tåquist (Uppsala University, Sweden)

Implementations of stateful security protocols must carefully manage the type and order of exchanged messages and cryptographic material, by maintaining a state machine which keeps track of protocol progress. Corresponding implementation flaws, called emph{state machine bugs}, can constitute serious security vulnerabilities. We present an automated black-box technique for detecting state machine bugs in implementations of stateful network protocols. It takes as input a catalogue of state machine bugs for the protocol, each specified as a finite automaton which accepts sequences of messages that exhibit the bug, and a (possibly inaccurate) model of the implementation under test, typically obtained by model learning. Our technique constructs the set of sequences that (according to the model) can be performed by the implementation and that (according to the automaton) expose the bug. These sequences are then transformed to test cases on the actual implementation to find a witness for the bug or filter out false alarms. We have applied our technique on three widely-used implementations of SSH servers and nine different DTLS server and client implementations, including their most recent versions. Our technique easily reproduced all bugs identified by security researchers before, and produced witnesses for them. More importantly, it revealed several previously unknown bugs in the same implementations, two new vulnerabilities, and a variety of new bugs and non-conformance issues in newer versions of the same SSH and DTLS implementations.

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Cyber Threat Intelligence for SOC Analysts

Nidhi Rastogi, Md Tanvirul Alam (Rochester Institute of Technology)

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How to Count Bots in Longitudinal Datasets of IP...

Leon Böck (Technische Universität Darmstadt), Dave Levin (University of Maryland), Ramakrishna Padmanabhan (CAIDA), Christian Doerr (Hasso Plattner Institute), Max Mühlhäuser (Technical University of Darmstadt)

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Fusion: Efficient and Secure Inference Resilient to Malicious Servers

Caiqin Dong (Jinan University), Jian Weng (Jinan University), Jia-Nan Liu (Jinan University), Yue Zhang (Jinan University), Yao Tong (Guangzhou Fongwell Data Limited Company), Anjia Yang (Jinan University), Yudan Cheng (Jinan University), Shun Hu (Jinan University)

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