References of "State, Radu 50003137"
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See detailA Tale of Location-Based User Authentication
Falk, Eric UL; Toth, Vendel; Knaff, Alexandre et al

in IEEE BigComp2019 - The 6th IEEE International Conference on Big Data and Smart Computing (2019)

The attitude towards passwords has drastically changed over the past years. Although they protected workstations from illicit access for decades, with today’s increased computational power, simple ... [more ▼]

The attitude towards passwords has drastically changed over the past years. Although they protected workstations from illicit access for decades, with today’s increased computational power, simple passwords became easy targets for attacks, whereas complex passwords are difficult to remember for the users. It appears as if the classical password protection has become obsolete and has to give way to similarly secured schemes, which are seamless for users. Novel methodologies may be sound and secure from a technical point of view, their success will be challenged by the simple question whether a user feels secure or not. In this work, we propose a proximity based login and session locking scheme, based on bluetooth beacons. We describe the big data architecture required to implement secured location-based services in smart buildings. To round our contribution out, we describe a medium scale user study with 40 participants, conducted to answer the question: Do users feel secure? [less ▲]

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See detailOsiris: Hunting for Integer Bugs in Ethereum Smart Contracts
Ferreira Torres, Christof UL; Schütte, Julian; State, Radu UL

in 34th Annual Computer Security Applications Conference (ACSAC ’18), San Juan, Puerto Rico, USA, December 3-7, 2018 (2018, December)

The capability of executing so-called smart contracts in a decentralised manner is one of the compelling features of modern blockchains. Smart contracts are fully fledged programs which cannot be changed ... [more ▼]

The capability of executing so-called smart contracts in a decentralised manner is one of the compelling features of modern blockchains. Smart contracts are fully fledged programs which cannot be changed once deployed to the blockchain. They typically implement the business logic of distributed apps and carry billions of dollars worth of coins. In that respect, it is imperative that smart contracts are correct and have no vulnerabilities or bugs. However, research has identified different classes of vulnerabilities in smart contracts, some of which led to prominent multi-million dollar fraud cases. In this paper we focus on vulnerabilities related to integer bugs, a class of bugs that is particularly difficult to avoid due to some characteristics of the Ethereum Virtual Machine and the Solidity programming language. In this paper we introduce Osiris – a framework that combines symbolic execution and taint analysis, in order to accurately find integer bugs in Ethereum smart contracts. Osiris detects a greater range of bugs than existing tools, while providing a better specificity of its detection. We have evaluated its performance on a large experimental dataset containing more than 1.2 million smart contracts. We found that 42,108 contracts contain integer bugs. Be- sides being able to identify several vulnerabilities that have been reported in the past few months, we were also able to identify a yet unknown critical vulnerability in a couple of smart contracts that are currently deployed on the Ethereum blockchain. [less ▲]

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See detailBlockPGP: A Blockchain-based Framework for PGP Key Servers
Yakubov, Alexander UL; Shbair, Wazen UL; State, Radu UL

in The Sixth International Symposium on Computing and Networking (2018, November 28)

Pretty Good Privacy (PGP) is one of the most prominent cryptographic standards, offering end-to-end encryption for email messages and other sensitive information. PGP allows to verify the identity of the ... [more ▼]

Pretty Good Privacy (PGP) is one of the most prominent cryptographic standards, offering end-to-end encryption for email messages and other sensitive information. PGP allows to verify the identity of the correspondent in information exchange as well as the information integrity. It implements asymmetric encryption with certificates shared through a network of PGP key servers. Many recent breaches show that certificate infrastructure can be compromised as well as exposed to operational errors. In this paper, we propose a new PGP management framework with the key server infrastructure implemented using blockchain technology. Our framework resolves some problems of PGP key servers focusing in particular on fast propagation of certificate revocation among key servers and elimination of man-in-the-middle risk. We also provided user access right control where only the certificate holder can change information related to the certificate. We designed and developed a prototype for key server deployment on permissioned Ethereum blockchain. Permissioned blockchain should allow to control the costs of PGP key server infrastructure maintenance at the present level. [less ▲]

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See detailVisualizing the Learning Progress of Self-Driving Cars
Mund, Sandro; Frank, Raphaël UL; Varisteas, Georgios UL et al

in 21st International Conference on Intelligent Transportation Systems (2018, November 02)

Using Deep Learning to predict lateral and longitudinal vehicle control, i.e. steering, acceleration and braking, is becoming increasingly popular. However, it remains widely unknown why those models ... [more ▼]

Using Deep Learning to predict lateral and longitudinal vehicle control, i.e. steering, acceleration and braking, is becoming increasingly popular. However, it remains widely unknown why those models perform so well. In order for them to become a commercially viable solution, it first needs to be understood why a certain behavior is triggered and how and what those networks learn from human-generated driving data to ensure safety. One research direction is to visualize what the network sees by highlighting regions of an image that influence the outcome of the model. In this vein, we propose a generic visualization method using Attention Heatmaps (AHs) to highlight what a given Convolutional Neural Network (CNN) learns over time. To do so, we rely on a novel occlusion technique to mask different regions of an input image to observe the effect on a predicted steering signal. We then gradually increase the amount of training data and study the effect on the resulting Attention Heatmaps, both in terms of visual focus and temporal behavior. [less ▲]

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See detailUser-Device Authentication in Mobile Banking using APHEN for Paratuck2 Tensor Decomposition
Charlier, Jérémy Henri J. UL; Falk, Eric UL; State, Radu UL et al

in 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (2018)

The new financial European regulations such as PSD2 are changing the retail banking services. Noticeably, the monitoring of the personal expenses is now opened to other institutions than retail banks ... [more ▼]

The new financial European regulations such as PSD2 are changing the retail banking services. Noticeably, the monitoring of the personal expenses is now opened to other institutions than retail banks. Nonetheless, the retail banks are looking to leverage the user-device authentication on the mobile banking applications to enhance the personal financial advertisement. To address the profiling of the authentication, we rely on tensor decomposition, a higher dimensional analogue of matrix decomposition. We use Paratuck2, which expresses a tensor as a multiplication of matrices and diagonal tensors, because of the imbalance between the number of users and devices. We highlight why Paratuck2 is more appropriate in this case than the popular CP tensor decomposition, which decomposes a tensor as a sum of rank-one tensors. However, the computation of Paratuck2 is computational intensive. We propose a new APproximate HEssian-based Newton resolution algorithm, APHEN, capable of solving Paratuck2 more accurately and faster than the other popular approaches based on alternating least square or gradient descent. The results of Paratuck2 are used for the predictions of users' authentication with neural networks. We apply our method for the concrete case of targeting clients for financial advertising campaigns based on the authentication events generated by mobile banking applications. [less ▲]

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See detailKnowledge Discovery Approach from Blockchain, Crypto-currencies, and Financial Stock Exchanges
Lagraa, Sofiane UL; Charlier, Jérémy Henri J. UL; State, Radu UL

Poster (2018, August 20)

Last few years have witnessed a steady growth in interest on crypto-currencies and blockchains. They are receiving considerable interest from industry and the research community, the most popular one ... [more ▼]

Last few years have witnessed a steady growth in interest on crypto-currencies and blockchains. They are receiving considerable interest from industry and the research community, the most popular one being Bitcoin. However, these crypto-currencies are so far relatively poorly analyzed and investigated. Recently, many solutions, mostly based on ad-hoc engineered solutions, are being developed to discover relevant analysis from crypto-currencies, but are not sufficient to understand behind crypto-currencies. In this paper, we provide a deep analysis of crypto-currencies by proposing a new knowledge discovery approach for each crypto-currency, across crypto-currencies, blockchains, and financial stocks. The novel approach is based on a conjoint use of data mining algorithms on imbalanced time series. It automatically reports co-variation dependency patterns of the time series. The experiments on the public crypto-currencies and financial stocks markets data also demonstrate the usefulness of the approach by discovering the different relationships across multiple time series sources and insights correlations behind crypto-currencies. [less ▲]

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See detailBlockchain-Based, Decentralized Access Control for IPFS
Steichen, Mathis UL; Fiz Pontiveros, Beltran UL; Norvill, Robert UL et al

in The 2018 IEEE International Conference on Blockchain (Blockchain-2018) (2018, July 30)

Large files cannot be efficiently stored on blockchains. On one hand side, the blockchain becomes bloated with data that has to be propagated within the blockchain network. On the other hand, since the ... [more ▼]

Large files cannot be efficiently stored on blockchains. On one hand side, the blockchain becomes bloated with data that has to be propagated within the blockchain network. On the other hand, since the blockchain is replicated on many nodes, a lot of storage space is required without serving an immediate purpose, especially if the node operator does not need to view every file that is stored on the blockchain. It furthermore leads to an increase in the price of operating blockchain nodes because more data needs to be processed, transferred and stored. IPFS is a file sharing system that can be leveraged to more efficiently store and share large files. It relies on cryptographic hashes that can easily be stored on a blockchain. Nonetheless, IPFS does not permit users to share files with selected parties. This is necessary, if sensitive or personal data needs to be shared. Therefore, this paper presents a modified version of the InterPlanetary Filesystem (IPFS) that leverages Ethereum smart contracts to provide access controlled file sharing. The smart contract is used to maintain the access control list, while the modified IPFS software enforces it. For this, it interacts with the smart contract whenever a file is uploaded, downloaded or transferred. Using an experimental setup, the impact of the access controlled IPFS is analyzed and discussed. [less ▲]

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See detailVisual emulation for Ethereum's virtual machine
Norvill, Robert UL; Fiz Pontiveros, Beltran UL; State, Radu UL et al

in NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium (2018, July 09)

In this work we present E-EVM, a tool that emulates and visualises the execution of smart contracts on the Ethereum Virtual Machine. By working with the readily available bytecode of smart contracts we ... [more ▼]

In this work we present E-EVM, a tool that emulates and visualises the execution of smart contracts on the Ethereum Virtual Machine. By working with the readily available bytecode of smart contracts we are able to display the program's control flow graph, opcodes and stack for each step of contract execution. This tool is designed to aid the user's understanding of the Etheruem Virtual Machine as well as aid the analysis of any given smart contract. As such, it functions as both an analysis and a learning tool. It allows the user to view the code in each block of a smart contract and follow possible control flow branches. It is able to detect loops and suggest optimisation candidates. It is possible to step through a contract one opcode at a time. E-EVM achieved an average of 85.6% code coverage when tested. [less ▲]

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See detailGenerating Multi-Categorical Samples with Generative Adversarial Networks
Camino, Ramiro Daniel UL; Hammerschmidt, Christian UL; State, Radu UL

Scientific Conference (2018, July)

We propose a method to train generative adversarial networks on mutivariate feature vectors representing multiple categorical values. In contrast to the continuous domain, where GAN-based methods have ... [more ▼]

We propose a method to train generative adversarial networks on mutivariate feature vectors representing multiple categorical values. In contrast to the continuous domain, where GAN-based methods have delivered considerable results, GANs struggle to perform equally well on discrete data. We propose and compare several architectures based on multiple (Gumbel) softmax output layers taking into account the structure of the data. We evaluate the performance of our architecture on datasets with different sparsity, number of features, ranges of categorical values, and dependencies among the features. Our proposed architecture and method outperforms existing models. [less ▲]

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See detailNon-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network for Smart Contracts Profiling
Charlier, Jérémy Henri J. UL; State, Radu UL

in International Journal of Computer & Software Engineering (2018), 3(1),

Background: Past few months have seen the rise of blockchain and cryptocurrencies. In this context, the Ethereum platform, an open-source blockchain-based platform using Ether cryptocurrency, has been ... [more ▼]

Background: Past few months have seen the rise of blockchain and cryptocurrencies. In this context, the Ethereum platform, an open-source blockchain-based platform using Ether cryptocurrency, has been designed to use smart contracts programs. These are self-executing blockchain contracts. Due to their high volume of transactions, analyzing their behavior is very challenging. We address this challenge in our paper. Methods: We develop for this purpose an innovative approach based on the non-negative tensor decomposition Paratuck2 combined with long short-term memory. The objective is to assess if predictive analysis can forecast smart contracts activities over time. Three statistical tests are performed on the predictive analytics, the mean absolute percentage error, the mean directional accuracy and the Jaccard distance. Results: Among dozens of GB of transactions, the Paratuck2 tensor decomposition allows asymmetric modeling of the smart contracts. Furthermore, it highlights time dependent latent groups. The latent activities are modeled by the long short term memory network for predictive analytics. The highly accurate predictions underline the accuracy of the method and show that blockchain activities are not pure randomness. Conclusion: Herein, we are able to detect the most active contracts, and predict their behavior. In the context of future regulations, our approach opens new perspective for monitoring blockchain activities. [less ▲]

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See detailRecycling Smart Contracts: Compression of the Ethereum Blockchain.
Fiz Pontiveros, Beltran UL; Norvill, Robert UL; State, Radu UL

in Proceedings of 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) 2018 (2018, February)

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See detailNon-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling
Charlier, Jérémy Henri J. UL; State, Radu UL; Hilger, Jean

in Charlier, Jeremy; State, Radu; Hilger, Jean (Eds.) 2018 IEEE International Conference on Big Data and Smart Computing Proceedings (2018, January)

Smart contracts are programs stored and executed on a blockchain. The Ethereum platform, an open source blockchain-based platform, has been designed to use these programs offering secured protocols and ... [more ▼]

Smart contracts are programs stored and executed on a blockchain. The Ethereum platform, an open source blockchain-based platform, has been designed to use these programs offering secured protocols and transaction costs reduction. The Ethereum Virtual Machine performs smart contracts runs, where the execution of each contract is limited to the amount of gas required to execute the operations described in the code. Each gas unit must be paid using Ether, the crypto-currency of the platform. Due to smart contracts interactions evolving over time, analyzing the behavior of smart contracts is very challenging. We address this challenge in our paper. We develop for this purpose an innovative approach based on the nonnegative tensor decomposition PARATUCK2 combined with long short-term memory (LSTM) to assess if predictive analysis can forecast smart contracts interactions over time. To validate our methodology, we report results for two use cases. The main use case is related to analyzing smart contracts and allows shedding some light into the complex interactions among smart contracts. In order to show the generality of our method on other use cases, we also report its performance on video on demand recommendation. [less ▲]

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See detailBlockchain Orchestration and Experimentation Framework: A Case Study of KYC
Shbair, Wazen UL; Steichen, Mathis UL; François, Jérôme UL et al

in The First IEEE/IFIP International Workshop on Managing and Managed by Blockchain (Man2Block) colocated with IEEE/IFIP NOMS 2018 (2018)

Conducting experiments to evaluate blockchain applications is a challenging task for developers, because there is a range of configuration parameters that control blockchain environments. Many public ... [more ▼]

Conducting experiments to evaluate blockchain applications is a challenging task for developers, because there is a range of configuration parameters that control blockchain environments. Many public testnets (e.g. Rinkeby Ethereum) can be used for testing, however, we cannot adjust their parameters (e.g. Gas limit, Mining difficulty) to further the understanding of the application in question and of the employed blockchain. This paper proposes an easy to use orchestration framework over the Grid'5000 platform. Grid'5000 is a highly reconfigurable and controllable large-scale testbed. We developed a tool that facilitates nodes reservation, deployment and blockchain configuration over the Grid'5000 platform. In addition, our tool can fine-tune blockchain and network parameters before and between experiments. The proposed framework offers insights for private and consortium blockchain developers to identify performance bottlenecks and to assess the behavior of their applications in different circumstances. [less ▲]

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See detailDistributed C++-Python embedding for fast predictions and fast prototyping
Varisteas, Georgios UL; Avanesov, Tigran UL; State, Radu UL

in Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning (2018)

Python has evolved to become the most popular language for data science. It sports state-of-the-art libraries for analytics and machine learning, like Sci-Kit Learn. However, Python lacks the ... [more ▼]

Python has evolved to become the most popular language for data science. It sports state-of-the-art libraries for analytics and machine learning, like Sci-Kit Learn. However, Python lacks the computational performance that a industrial system requires for high frequency real time predictions. Building upon a year long research project heavily based on SciKit Learn (sklearn), we faced performance issues in deploying to production. Replacing sklearn with a better performing framework would require re-evaluating and tuning hyperparameters from scratch. Instead we developed a python embedding in a C++ based server application that increased performance by up to 20x, achieving linear scalability up to a point of convergence. Our implementation was done for mainstream cost effective hardware, which means we observed similar performance gains on small as well as large systems, from a laptop to an Amazon EC2 instance to a high-end server. [less ▲]

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See detailMachine Learning for Data-Driven Smart Grid Applications
Glauner, Patrick UL; Meira, Jorge Augusto UL; State, Radu UL

Scientific Conference (2018)

The field of Machine Learning grew out of the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns ... [more ▼]

The field of Machine Learning grew out of the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns can then be applied to new data in order to make predictions. Machine Learning also allows to automatically adapt to changes in the data without amending the underlying model. We deal every day dozens of times with Machine Learning applications such as when doing a Google search, using spam filters, face detection, speaking to voice recognition software or when sitting in a self-driving car. In recent years, machine learning methods have evolved in the smart grid community. This change towards analyzing data rather than modeling specific problems has lead to adaptable, more generic methods, that require less expert knowledge and that are easier to deploy in a number of use cases. This is an introductory level course to discuss what machine learning is and how to apply it to data-driven smart grid applications. Practical case studies on real data sets, such as load forecasting, detection of irregular power usage and visualization of customer data, will be included. Therefore, attendees will not only understand, but rather experience, how to apply machine learning methods to smart grid data. [less ▲]

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See detailDetecting Malicious Authentication Events Trustfully
Kaiafas, Georgios UL; Varisteas, Georgios UL; Lagraa, Sofiane UL et al

in Kaiafas, Georgios; Varisteas, Georgios; Lagraa, Sofiane (Eds.) et al IEEE/IFIP Network Operations and Management Symposium, 23-27 April 2018, Taipei, Taiwan Cognitive Management in a Cyber World (2018)

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See detailTowards a Management Plane for Smart Contracts: Ethereum Case Study
Khan, Nida UL; Lahmadi, Abdelkader; Francois, Jerome et al

in NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium (2018)

Blockchain is an emerging foundational technology with the potential to create a novel economic and social system. The complexity of the technology poses many challenges and foremost amongst these are ... [more ▼]

Blockchain is an emerging foundational technology with the potential to create a novel economic and social system. The complexity of the technology poses many challenges and foremost amongst these are monitoring and management of blockchain-based decentralized applications. In this paper, we design, implement and evaluate a novel system to enable management operations in smart contracts. A key aspect of our system is that it facilitates the integration of these operations through dedicated ’managing’ smart contracts to provide data filtering as per the role of the smart contract-based application user. We evaluate the overhead costs of such data filtering operations after post-deployment analyses of five categories of smart contracts on the Ethereum public testnet, Rinkeby. We also build a monitoring tool to display public blockchain data using a dashboard coupled with a notification mechanism of any changes in private data to the administrator of the monitored decentralized application. [less ▲]

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See detailImpact of Biases in Big Data
Glauner, Patrick UL; Valtchev, Petko; State, Radu UL

in Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) (2018)

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is ... [more ▼]

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems. [less ▲]

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See detailIntroduction to Machine Learning for Power Engineers
Glauner, Patrick UL; State, Radu UL

Scientific Conference (2018)

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See detailMonitoring the transaction selection policy of Bitcoin mining pools
Fiz Pontiveros, Beltran UL; Norvill, Robert UL; State, Radu UL

in NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium (2018)

Mining pools are collection of workers that work together as a group in order to collaborate in the proof of work and reduce the variance of their rewards when mining. In order to achieve this, Mining ... [more ▼]

Mining pools are collection of workers that work together as a group in order to collaborate in the proof of work and reduce the variance of their rewards when mining. In order to achieve this, Mining pools distribute amongst the workers the task of finding a block so that each worker works on a different subset of the candidate solutions. In most mining pools the selection of transactions to be part of the next block is performed by the pool manager and thus becomes more centralized. A mining Pool is expected to give priority to the most lucrative transactions in order to increase the block reward however changes to the transaction policy done without notification of workers would be difficult to detect. In this paper we treat the transaction selection policy performed by miners as a classification problem; for each block we create a dataset, separate them by mining pool and apply feature selection techniques to extract a vector of importance for each feature. We then track variations in feature importance as new blocks arrive and show using a generated scenario how a change in policy by a mining pool could be detected. [less ▲]

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