Yue Duan (Cornell University), Xuezixiang Li (UC Riverside), Jinghan Wang (UC Riverside), Heng Yin (UC Riverside)

Binary diffing analysis quantitatively measures the differences between two given binaries and produces fine-grained basic block matching. It has been widely used to enable different kinds of critical security analysis. However, all existing program analysis and machine learning based techniques suffer from low accuracy, poor scalability, coarse granularity, or require extensive labeled training data to function. In this paper, we propose an unsupervised program-wide code representation learning technique to solve the problem. We rely on both the code semantic information and the program-wide control flow information to generate block embeddings. Furthermore, we propose a k-hop greedy matching algorithm to find the optimal diffing results using the generated block embeddings. We implement a prototype called DeepBinDiff and evaluate its effectiveness and efficiency with large number of binaries. The results show that our tool could outperform the state-of-the-art binary diffing tools by a large margin for both cross-version and cross-optimization level diffing. A case study for OpenSSL using real-world vulnerabilities further demonstrates the usefulness of our system.

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Decentralized Control: A Case Study of Russia

Reethika Ramesh (University of Michigan), Ram Sundara Raman (University of Michgan), Matthew Bernhard (University of Michigan), Victor Ongkowijaya (University of Michigan), Leonid Evdokimov (Independent), Anne Edmundson (Independent), Steven Sprecher (University of Michigan), Muhammad Ikram (Macquarie University), Roya Ensafi (University of Michigan)

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BLAZE: Blazing Fast Privacy-Preserving Machine Learning

Arpita Patra (Indian Institute of Science, Bangalore), Ajith Suresh (Indian Institute of Science, Bangalore)

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A Practical Approach for Taking Down Avalanche Botnets Under...

Victor Le Pochat (imec-DistriNet, KU Leuven), Tim Van hamme (imec-DistriNet, KU Leuven), Sourena Maroofi (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG), Tom Van Goethem (imec-DistriNet, KU Leuven), Davy Preuveneers (imec-DistriNet, KU Leuven), Andrzej Duda (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG), Wouter Joosen (imec-DistriNet, KU Leuven), Maciej Korczyński (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG)

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Post-Quantum Authentication in TLS 1.3: A Performance Study

Dimitrios Sikeridis (The University of New Mexico), Panos Kampanakis (Cisco Systems), Michael Devetsikiotis (The University of New Mexico)

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