14 citations · 41 across the 13 of their papers we have counts for
14 papers
JABBERWOCK: A Tool for WebAssembly Dataset Generation and Its Application to Malicious Website Detection
Chika Komiya, Naoto Yanai, Kyosuke Yamashita +1
Machine learning is often used for malicious website detection, but an approach incorporating WebAssembly as a feature has not been explored due to a limited number of samples, to…
An Empirical Study of Impact of Solidity Compiler Updates on Vulnerabilities in Ethereum Smart Contracts
Chihiro Kado, Naoto Yanai, Jason Paul Cruz +2
Vulnerabilities of Ethereum smart contracts often cause serious financial damage. Whereas the Solidity compiler has been updated to prevent vulnerabilities, its effectiveness has n…
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning
Yumeki Goto, Tomoya Matsumoto, Hamada Rizk +2
Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportati…
Do Backdoors Assist Membership Inference Attacks?
Yumeki Goto, Nami Ashizawa, Toshiki Shibahara +1
When an adversary provides poison samples to a machine learning model, privacy leakage, such as membership inference attacks that infer whether a sample was included in the trainin…
Membership Inference Attacks against Diffusion Models
Tomoya Matsumoto, Takayuki Miura, Naoto Yanai
Diffusion models have attracted attention in recent years as innovative generative models. In this paper, we investigate whether a diffusion model is resistant to a membership infe…
IoT-REX: A Secure Remote-Control System for IoT Devices from Centralized Multi-Designated Verifier Signatures
Yohei Watanabe, Naoto Yanai, Junji Shikata
IoT technology has been developing rapidly, while at the same time, notorious IoT malware such as Mirai is a severe and inherent threat. We believe it is essential to consider syst…
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…
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…
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'…
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…
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…
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…
Key-Aggregate Searchable Encryption, Revisited: Formal Foundations for Cloud Applications, and Their Implementation
Masahiro Kamimura, Naoto Yanai, Shingo Okamura +1
In the use of a cloud storage, sharing of data with efficient access control is an important requirement in addition to data security and privacy. Cui et al. (IEEE Trans. on Comp.…
MOBIUS: Model-Oblivious Binarized Neural Networks
Hiromasa Kitai, Jason Paul Cruz, Naoto Yanai +7
A privacy-preserving framework in which a computational resource provider receives encrypted data from a client and returns prediction results without decrypting the data, i.e., ob…