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20192026
most citedLabel Smoothing and Adversarial Robustness

10 citations · 38 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CR2026

PDoS: A Profitable Denial-of-Service Attack against Proof-of-Work Blockchain Liveness

Junjie Hu, Tianzhu Han, Na Ruan

The security and liveness of Proof-of-Work (PoW) blockchains fundamentally depend on the economic rationality of miners. Existing incentive-driven denial-of-service attacks, such a…

cs.CR2026

Blockchain Attacks and Defenses: A Layered and Cross-Domain Survey

Junjie Hu, Na Ruan

Blockchains have evolved from simple distributed ledgers into programmable platforms that process complex application logic and carry significant financial value. All modern Web3 s…

cs.CR2022★ 2 cited

Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework

Jikun Chen, Feng Qiang, Na Ruan

The performance of deep learning models highly depends on the amount of training data. It is common practice for today's data holders to merge their datasets and train models colla…

cs.CR2019★ 7 cited

FraudJudger: Real-World Data Oriented Fraud Detection on Digital Payment Platforms

Ruoyu Deng, Na Ruan

Automated fraud behaviors detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of use…

cs.CR2019★ 1 cited

Catfish Effect Between Internal and External Attackers:Being Semi-honest is Helpful

Hanqing Liu, Na Ruan, Joseph K. Liu

The consensus protocol named proof of work (PoW) is widely applied by cryptocurrencies like Bitcoin. Although security of a PoW cryptocurrency is always the top priority, it is thr…