Adam Humphries (University of North Carolina), Kartik Cating-Subramanian (University of Colorado), Michael K. Reiter (Duke University)

We present the design and implementation of a tool called TASE that uses transactional memory to reduce the latency of symbolic-execution applications with small amounts of symbolic state.
Execution paths are executed natively while operating on concrete values, and only when execution encounters symbolic values (or modeled functions) is native execution suspended and interpretation begun. Execution then returns to its native mode when symbolic values are no longer encountered. The key innovations in the design of TASE are a technique for amortizing the cost of checking whether values are symbolic over few instructions, and the use of hardware-supported transactional memory (TSX) to implement native execution that rolls back with no effect when use of a symbolic value is detected (perhaps belatedly). We show that TASE has the potential to dramatically improve some latency-sensitive applications of symbolic execution, such as methods to verify the behavior of a client in a client-server application.

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FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data

Junjie Liang (The Pennsylvania State University), Wenbo Guo (The Pennsylvania State University), Tongbo Luo (Robinhood), Vasant Honavar (The Pennsylvania State University), Gang Wang (University of Illinois at Urbana-Champaign), Xinyu Xing (The Pennsylvania State University)

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SODA: A Generic Online Detection Framework for Smart Contracts

Ting Chen (University of Electronic Science and Technology of China), Rong Cao (University of Electronic Science and Technology of China), Ting Li (University of Electronic Science and Technology of China), Xiapu Luo (The Hong Kong Polytechnic University), Guofei Gu (Texas A&M University), Yufei Zhang (University of Electronic Science and Technology of China), Zhou Liao (University…

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Serge Vaudenay, EPFL, Switzerland

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Mohsen Ahmadi (Arizona State University), Pantea Kiaei (Worcester Polytechnic Institute), Navid Emamdoost (University of Minnesota)

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