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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PhantomCache: Obfuscating Cache Conflicts with Localized Randomization

Qinhan Tan (Zhejiang University), Zhihua Zeng (Zhejiang University), Kai Bu (Zhejiang University), Kui Ren (Zhejiang University)

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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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Cross-Origin State Inference (COSI) Attacks: Leaking Web Site States...

Avinash Sudhodanan (IMDEA Software Institute), Soheil Khodayari (CISPA Helmholtz Center for Information Security), Juan Caballero (IMDEA Software Institute)

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