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Found 10 papers, 5 papers with code
JABBERWOCK: A Tool for WebAssembly Dataset Generation and Its Application to Malicious Website Detection
We also conduct experimental evaluations of JABBERWOCK in terms of the processing time for dataset generation, comparison of the generated samples with actual WebAssembly samples gathered from the Internet, and an application for malicious website detection.
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning
Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportation infrastructure and services.
Do Backdoors Assist Membership Inference Attacks?
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 training of the model, becomes effective by moving the sample to an outlier.
Membership Inference Attacks against Diffusion Models
We primarily discuss the diffusion model from the standpoints of comparison with a generative adversarial network (GAN) as conventional models and hyperparameters unique to the diffusion model, i. e., time steps, sampling steps, and sampling variances.
First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data
Whereas model extraction is more challenging on tabular data due to normalization, TEMPEST no longer needs initial samples that previous attacks require; instead, it makes use of publicly available statistics to generate query samples.
Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts
Therefore, Eth2Vec can detect vulnerabilities in smart contracts by comparing the code similarity between target EVM bytecodes and the EVM bytecodes it already learned.
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection
In this paper, we propose a self-organizing map assisted deep autoencoding Gaussian mixture model (SOMDAGMM) supplemented with well-preserved input space topology for more accurate network intrusion detection.
Hunting for Re-Entrancy Attacks in Ethereum Smart Contracts via Static Analysis
Ethereum smart contracts are programs that are deployed and executed in a consensus-based blockchain managed by a peer-to-peer network.
Cryptography and Security
Model Extraction Attacks against Recurrent Neural Networks
First, in a case of a classification problem, such as image recognition, extraction of an RNN model without final outputs from an LSTM model is presented by utilizing outputs halfway through the sequence.
MOBIUS: Model-Oblivious Binarized Neural Networks
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., oblivious neural network or encrypted prediction, has been studied in machine learning that provides prediction services.
