1 code implementation • 16 Nov 2018 • Wesley Joon-Wie Tann, Xing Jie Han, Sourav Sen Gupta, Yew-Soon Ong
In particular, we propose a novel approach of sequential learning of smart contract vulnerabilities using machine learning --- long-short term memory (LSTM) --- that perpetually learns from an increasing number of contracts handled over time, leading to safer smart contracts.
Cryptography and Security
no code implementations • 6 Jun 2020 • Wesley Joon-Wie Tann, Ee-Chien Chang, Bryan Hooi
Given an observed graph and some user-specified Markov model parameters, ${\rm S{\small HADOW}C{\small AST}}$ controls the conditions to generate desired graphs.
no code implementations • 12 Dec 2020 • Wesley Joon-Wie Tann, Jackie Tan Jin Wei, Joanna Purba, Ee-Chien Chang
We also introduce a machine learning optimization problem that aims to sift out the attacks using ${\mathcal N}$ and ${\mathcal M}$.
no code implementations • 28 Sep 2020 • Wesley Joon-Wie Tann, Ee-Chien Chang, Bryan Hooi
We introduce the problem of explaining graph generation, formulated as controlling the generative process to produce desired graphs with explainable structures.
no code implementations • 30 Nov 2021 • Jiyi Zhang, Han Fang, Wesley Joon-Wie Tann, Ke Xu, Chengfang Fang, Ee-Chien Chang
We point out that by distributing different copies of the model to different buyers, we can mitigate the attack such that adversarial samples found on one copy would not work on another copy.
no code implementations • 14 Oct 2022 • Wesley Joon-Wie Tann, Akhil Vuputuri, Ee-Chien Chang
In this paper, we want to obtain a generative model that, given the early transactions history (first quarter Q1) of a newly minted collection, generates subsequent transactions (quarters Q2, Q3, Q4), where the generative model is trained using the transaction history of a few mature collections.