Minkyu Jung (KAIST), Soomin Kim (KAIST), HyungSeok Han (KAIST), Jaeseung Choi (KAIST), Sang Kil Cha (KAIST)

Current binary analysis research focuses mainly on the back-end, but not on the front-end. However, we note that there are several key design points in the front-end that can greatly improve the efficiency of binary analyses. To demonstrate our idea, we design and implement B2R2, a new binary analysis platform that is fast with regard to lifting binary code and evaluating the corresponding IR. Our platform is written purely in F#, a functional programming language, without any external dependencies. Thus, it naturally supports pure parallelism. B2R2’s IR embeds metadata in its language for speeding up dataflow analyses, and it is designed to be efficient for evaluation. Therefore, any binary analysis technique can benefit from our IR design. We discuss our design decisions to build an efficient binary analysis front-end, and summarize lessons learned. We also make our source code public on GitHub.

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Investigating Graph Embedding Neural Networks with Unsupervised Features Extraction...

Luca Massarelli (Sapienza University of Rome), Giuseppe A. Di Luna (CINI - National Laboratory of Cybersecurity), Fabio Petroni (Independent Researcher), Leonardo Querzoni (Sapienza University of Rome), Roberto Baldoni (Italian Presidency of Ministry Council)

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Binary Code Patching: An Ancient Art Refined for the...

Dr. Barton P. Miller (Vilas Distinguished Achievement Professor at The University of Wisconsin-Madison)

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Detecting Obfuscated Function Clones in Binaries using Machine Learning

Michael Pucher (University of Vienna), Christian Kudera (SBA Research), Georg Merzdovnik (SBA Research)

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Romain Malmain (EURECOM), Andrea Fioraldi (EURECOM), Aurelien Francillon (EURECOM)

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