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

The vulnerability of traditional blockchains have been demonstrated at multiple occasions. Various companies are now moving towards Proof-of-Authority (PoA) blockchains with more conventional Byzantine fault tolerance, where a known set of n permissioned sealers, among which no more than t are Byzantine, seal blocks that include user transactions. Despite their wide adoption, these protocols were not proved correct.

In this paper, we present the Cloning Attack against the two mostly deployed PoA implementations of Ethereum, namely Aura and Clique. The Cloning Attack consists of one sealer cloning its pair of public-private keys into two distinct Ethereum instances that communicate with distinct groups of sealers. To identify their vulnerabilities, we first specify the corresponding algorithms. We then deploy one testnet for each protocol and demonstrate the success of the attack with only one Byzantine sealer. Finally, we propose counter-measures that prevent an adversary from double spending and introduce the necessary number of sealers needed to decide a block depending on n and t for both Aura and Clique to be safe.

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HFL: Hybrid Fuzzing on the Linux Kernel

Kyungtae Kim (Purdue University), Dae R. Jeong (KAIST), Chung Hwan Kim (NEC Labs America), Yeongjin Jang (Oregon State University), Insik Shin (KAIST), Byoungyoung Lee (Seoul National University)

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CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples

Honggang Yu (University of Florida), Kaichen Yang (University of Florida), Teng Zhang (University of Central Florida), Yun-Yun Tsai (National Tsing Hua University), Tsung-Yi Ho (National Tsing Hua University), Yier Jin (University of Florida)

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Unicorn: Runtime Provenance-Based Detector for Advanced Persistent Threats

Xueyuan Han (Harvard University), Thomas Pasquier (University of Bristol), Adam Bates (University of Illinois at Urbana-Champaign), James Mickens (Harvard University), Margo Seltzer (University of British Columbia)

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DeepBinDiff: Learning Program-Wide Code Representations for Binary Diffing

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

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