b'Radu State'

Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain

Ethereum prospered the inception of a plethora of smart contract applica...
02/05/2021 ∙ by Christof Ferreira Torres, et al. ∙ 0 ∙ share

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The Eye of Horus: Spotting and Analyzing Attacks on Ethereum Smart Contracts

In recent years, Ethereum gained tremendously in popularity, growing fro...
01/15/2021 ∙ by Christof Ferreira Torres, et al. ∙ 0 ∙ share

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Towards Smart Hybrid Fuzzing for Smart Contracts

Smart contracts are Turing-complete programs that are executed across a ...
05/25/2020 ∙ by Christof Ferreira Torres, et al. ∙ 0 ∙ share

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Minority Class Oversampling for Tabular Data with Deep Generative Models

In practice, data scientists are often confronted with imbalanced data. ...
05/07/2020 ∙ by Ramiro Camino, et al. ∙ 0 ∙ share

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Blockchain Governance: An Overview and Prediction of Optimal Strategies using Nash Equilibrium

Blockchain governance is a subject of ongoing research and an interdisci...
03/20/2020 ∙ by Nida Khan, et al. ∙ 0 ∙ share

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A Data Science Approach for Honeypot Detection in Ethereum

Ethereum smart contracts have recently drawn a considerable amount of at...
10/03/2019 ∙ by Ramiro Camino, et al. ∙ 0 ∙ share

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SynGAN: Towards Generating Synthetic Network Attacks using GANs

The rapid digital transformation without security considerations has res...
08/26/2019 ∙ by Jeremy Charlier, et al. ∙ 0 ∙ share

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PHom-GeM: Persistent Homology for Generative Models

Generative neural network models, including Generative Adversarial Netwo...
05/23/2019 ∙ by Jeremy Charlier, et al. ∙ 0 ∙ share

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User-Device Authentication in Mobile Banking using APHEN for Paratuck2 Tensor Decomposition

The new financial European regulations such as PSD2 are changing the ret...
05/23/2019 ∙ by Jeremy Charlier, et al. ∙ 0 ∙ share

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Non-Negative PARATUCK2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling

Smart contracts are programs stored and executed on a blockchain. The Et...
05/23/2019 ∙ by Jeremy Charlier, et al. ∙ 0 ∙ share

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Infer Your Enemies and Know Yourself, Learning in Real-Time Bidding with Partially Observable Opponents

Real-time bidding, as one of the most popular mechanisms for selling onl...
02/28/2019 ∙ by Manxing Du, et al. ∙ 0 ∙ share

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Improving Missing Data Imputation with Deep Generative Models

Datasets with missing values are very common on industry applications, a...
02/27/2019 ∙ by Ramiro D. Camino, et al. ∙ 0 ∙ share

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Generating Multi-Categorical Samples with Generative Adversarial Networks

We propose a method to train generative adversarial networks on mutivari...
07/03/2018 ∙ by Ramiro Camino, et al. ∙ 0 ∙ share

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Impact of Biases in Big Data

The underlying paradigm of big data-driven machine learning reflects the...
03/02/2018 ∙ by Patrick Glauner, et al. ∙ 0 ∙ share

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On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage

In machine learning, a bias occurs whenever training sets are not repres...
01/17/2018 ∙ by Patrick Glauner, et al. ∙ 0 ∙ share

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Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations

Power grids are critical infrastructure assets that face non-technical l...
09/09/2017 ∙ by Patrick Glauner, et al. ∙ 0 ∙ share

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Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms

We present an interactive version of an evidence-driven state-merging (E...
07/28/2017 ∙ by Christian A. Hammerschmidt, et al. ∙ 0 ∙ share

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The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study

Which topics of machine learning are most commonly addressed in research...
03/29/2017 ∙ by Patrick Glauner, et al. ∙ 0 ∙ share

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Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?

Non-technical losses (NTL) occur during the distribution of electricity ...
02/13/2017 ∙ by Patrick Glauner, et al. ∙ 0 ∙ share

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Interpreting Finite Automata for Sequential Data

Automaton models are often seen as interpretable models. Interpretabilit...
11/21/2016 ∙ by Christian Albert Hammerschmidt, et al. ∙ 0 ∙ share

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Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets

Electricity theft is a major problem around the world in both developed ...
07/04/2016 ∙ by Patrick Glauner, et al. ∙ 0 ∙ share

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Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets

Non-technical losses (NTL) such as electricity theft cause significant h...
02/26/2016 ∙ by Patrick O. Glauner, et al. ∙ 0 ∙ share

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Radu State

Featured Co-authors

Jun Wang

Arthur Gervais

Henning Schulzrinne

Ying Wen

Sicco Verwer

Alexander I. Cowen-Rivers

Jeremy Charlier

Christof Ferreira Torres

Patrick Glauner

Petko Valtchev

Qin Lin

Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain

The Eye of Horus: Spotting and Analyzing Attacks on Ethereum Smart Contracts

Towards Smart Hybrid Fuzzing for Smart Contracts

Minority Class Oversampling for Tabular Data with Deep Generative Models

Blockchain Governance: An Overview and Prediction of Optimal Strategies using Nash Equilibrium

A Data Science Approach for Honeypot Detection in Ethereum

SynGAN: Towards Generating Synthetic Network Attacks using GANs

PHom-GeM: Persistent Homology for Generative Models

User-Device Authentication in Mobile Banking using APHEN for Paratuck2 Tensor Decomposition

Non-Negative PARATUCK2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling

Infer Your Enemies and Know Yourself, Learning in Real-Time Bidding with Partially Observable Opponents

Improving Missing Data Imputation with Deep Generative Models

Generating Multi-Categorical Samples with Generative Adversarial Networks

Impact of Biases in Big Data

On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage

Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations

Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms

The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study

Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?

Interpreting Finite Automata for Sequential Data

Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets

Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets