[en] 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.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN)
Disciplines :
Computer science
Author, co-author :
Charlier, Jérémy Henri J. ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
State, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Hilger, Jean; Banque et Caisse d'Epargne de l'Etat (BCEE)
External co-authors :
no
Language :
English
Title :
Non-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling
Publication date :
January 2018
Event name :
2018 IEEE International Conference on Big Data and Smart Computing
Event organizer :
IEEE BigComp 2018
Event place :
Shanghai, China
Event date :
from 15-01-2018 to 18-01-2018
Audience :
International
Main work title :
2018 IEEE International Conference on Big Data and Smart Computing Proceedings