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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 Proceedings of 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 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 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 detailModeling Smart Contracts Activities: A Tensor based Approach
Charlier, Jérémy Henri J. UL; State, Radu UL; Hilger, Jean

in Charlier, Jeremy; State, Radu; Hilger, Jean (Eds.) Proceedings of 2017 Future Technologies Conference (FTC), 29-30 November 2017, Vancouver, Canada (2017, November)

Smart contracts are autonomous software executing predefined conditions. Two of the biggest advantages of the smart contracts are secured protocols and transaction costs reduction. On the Ethereum ... [more ▼]

Smart contracts are autonomous software executing predefined conditions. Two of the biggest advantages of the smart contracts are secured protocols and transaction costs reduction. On the Ethereum platform, an open-source blockchainbased platform, smart contracts implement a distributed virtual machine on the distributed ledger. To avoid denial of service attacks and monetize the services, payment transactions are executed whenever code is being executed between contracts. It is thus natural to investigate if predictive analysis is capable to forecast these interactions. We have addressed this issue and proposed an innovative application of the tensor decomposition CANDECOMP/PARAFAC to the temporal link prediction of smart contracts. We introduce a new approach leveraging stochastic processes for series predictions based on the tensor decomposition that can be used for smart contracts predictive analytics. [less ▲]

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See detailYour Moves, Your Device: Establishing Behavior Profiles Using Tensors
Falk, Eric UL; Charlier, Jérémy Henri J. UL; State, Radu UL

in Advanced Data Mining and Applications - 13th International Conference, ADMA 2017 (2017, November)

Smartphones became a person's constant companion. As the strictly personal devices they are, they gradually enable the replacement of well established activities as for instance payments, two factor ... [more ▼]

Smartphones became a person's constant companion. As the strictly personal devices they are, they gradually enable the replacement of well established activities as for instance payments, two factor authentication or personal assistants. In addition, Internet of Things (IoT) gadgets extend the capabilities of the latter even further. Devices such as body worn fitness trackers allow users to keep track of daily activities by periodically synchronizing data with the smartphone and ultimately with the vendor's computational centers in the cloud. These fitness trackers are equipped with an array of sensors to measure the movements of the device, to derive information as step counts or make assessments about sleep quality. We capture the raw sensor data from wrist-worn activity trackers to model a biometric behavior profile of the carrier. We establish and present techniques to determine rather the original person, who trained the model, is currently wearing the bracelet or another individual. Our contribution is based on CANDECOMP/PARAFAC (CP) tensor decomposition so that computational complexity facilitates: the execution on light computational devices on low precision settings, or the migration to stronger CPUs or to the cloud, for high to very high granularity. This precision parameter allows the security layer to be adaptable, in order to be compliant with the requirements set by the use cases. We show that our approach identifies users with high confidence. [less ▲]

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See detailProfiling Smart Contracts Interactions Tensor Decomposition and Graph Mining.
Charlier, Jérémy Henri J. UL; Lagraa, Sofiane UL; State, Radu UL et al

in Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017) co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), Skopje, Macedonia, September 18, 2017. (2017, September)

Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bitcoin’s crypto-currency. They provide higher transaction security and ... [more ▼]

Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bitcoin’s crypto-currency. They provide higher transaction security and allow economy of scale through the automated process. Smart contracts provides inherent benefits for financial institutions such as investment banking, retail banking, and insurance. This technology is widely used within Ethereum, an open source block-chain platform, from which the data has been extracted to conduct the experiments. In this work, we propose an multi-dimensional approach to find and predict smart contracts interactions only based on their crypto-currency exchanges. This approach relies on tensor modeling combined with stochastic processes. It underlines actual exchanges between smart contracts and targets the predictions of future interactions among the community. The tensor analysis is also challenged with the latest graph algorithms to assess its strengths and weaknesses in comparison to a more standard approach. [less ▲]

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