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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Heterogeneous Private Information Retrieval

Hamid Mozaffari (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

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A View from the Cockpit: Exploring Pilot Reactions to...

Matthew Smith (University of Oxford), Martin Strohmeier (University of Oxford), Jonathan Harman (Vrije Universiteit Amsterdam), Vincent Lenders (armasuisse Science and Technology), Ivan Martinovic (University of Oxford)

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SVLAN: Secure & Scalable Network Virtualization

Jonghoon Kwon (ETH), Taeho Lee (ETH), Claude Hähni (ETH), Adrian Perrig (ETH)

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SymTCP: Eluding Stateful Deep Packet Inspection with Automated Discrepancy...

Zhongjie Wang (University of California, Riverside), Shitong Zhu (University of California, Riverside), Yue Cao (University of California, Riverside), Zhiyun Qian (University of California, Riverside), Chengyu Song (University of California, Riverside), Srikanth V. Krishnamurthy (University of California, Riverside), Kevin S. Chan (U.S. Army Research Lab), Tracy D. Braun (U.S. Army Research Lab)

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