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20182023
most citedModel Extraction Attacks against Recurrent Neural Networks

4 citations · 15 across the 10 of their papers we have counts for

collaborators

8 papers

cs.CR20212 cited

First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data

Masataka Tasumi, Kazuki Iwahana, Naoto Yanai +5

Model extraction attacks are a kind of attacks where an adversary obtains a machine learning model whose performance is comparable with one of the victim model through queries and…

cs.CR2021

Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts

Nami Ashizawa, Naoto Yanai, Jason Paul Cruz +1

Ethereum smart contracts are programs that run on the Ethereum blockchain, and many smart contract vulnerabilities have been discovered in the past decade. Many security analysis t…

cs.CR20203 cited

APVAS: Reducing Memory Size of AS\_PATH Validation by Using Aggregate Signatures

Ouyang Junjie, Naoto Yanai, Tatsuya Takemura +3

The \textit{BGPsec} protocol, which is an extension of the border gateway protocol (BGP), uses digital signatures to guarantee the validity of routing information. However, BGPsec'…

cs.LG2020

Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection

Yang Chen, Nami Ashizawa, Seanglidet Yean +2

In the information age, a secure and stable network environment is essential and hence intrusion detection is critical for any networks. In this paper, we propose a self-organizing…

cs.CR20204 cited

Hunting for Re-Entrancy Attacks in Ethereum Smart Contracts via Static Analysis

Yuichiro Chinen, Naoto Yanai, Jason Paul Cruz +1

Ethereum smart contracts are programs that are deployed and executed in a consensus-based blockchain managed by a peer-to-peer network. Several re-entrancy attacks that aim to stea…

cs.CR20204 cited

Model Extraction Attacks against Recurrent Neural Networks

Tatsuya Takemura, Naoto Yanai, Toru Fujiwara

Model extraction attacks are a kind of attacks in which an adversary obtains a new model, whose performance is equivalent to that of a target model, via query access to the target…