no code implementations • 22 Mar 2023 • Yumeki Goto, Nami Ashizawa, Toshiki Shibahara, Naoto Yanai
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.
1 code implementation • 7 Jan 2021 • Nami Ashizawa, Naoto Yanai, Jason Paul Cruz, Shingo Okamura
Therefore, Eth2Vec can detect vulnerabilities in smart contracts by comparing the code similarity between target EVM bytecodes and the EVM bytecodes it already learned.
1 code implementation • 28 Aug 2020 • Yang Chen, Nami Ashizawa, Seanglidet Yean, Chai Kiat Yeo, Naoto Yanai
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.