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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Parinya Ekparinya (University of Sydney), Vincent Gramoli (University of Sydney and CSIRO-Data61), Guillaume Jourjon (CSIRO-Data61)

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Packet-Level Signatures for Smart Home Devices

Rahmadi Trimananda (University of California, Irvine), Janus Varmarken (University of California, Irvine), Athina Markopoulou (University of California, Irvine), Brian Demsky (University of California, Irvine)

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Peng Wang (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), Yue Qin (Indiana University Bloomington), XiaoFeng Wang (Indiana University Bloomington)

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Detecting Probe-resistant Proxies

Sergey Frolov (University of Colorado Boulder), Jack Wampler (University of Colorado Boulder), Eric Wustrow (University of Colorado Boulder)

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