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Machine Learning for the Blockchain
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Machine Learning for the Blockchain

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 28584

Special Issue Editors

Dr. Georgios Siolas

E-Mail Website
Guest Editor
School of Electrical & Computer Engineering, National Technical University of Athens, Athens, Greece
Interests: artificial intelligence; machine learning; natural language processing; information retrieval; evolutionary algorithms
Dr. Georgios Alexandridis

E-Mail Website
Guest Editor
School of Electrical & Computer Engineering, National Technical University of Athens, Athens, Greece
Interests: deep learning; financial decision making; security and fraud detection; forecasting
Dr. Paraskevi Tzouveli

E-Mail Website
Guest Editor
School of Electrical & Computer Engineering, National Technical University of Athens, Athens, Greece
Interests: deep learning; image and video analysis; information retrieval; knowledge manipulation; watermarking; blockchain for social impact and ride-sharing economy

Special Issue Information

Dear Colleagues,

Machine learning (ML) and distributed ledgers (DL) are two of today's most innovative technologies. The first is the evolution of statistics, artificial intelligence, and big data analysis, and the second is a decentralized database paradigm that has significantly disrupted the financial industry. Blockchain is currently the prominent but not the only DL solution and at the basis of the flourishing crypto ecosystem.

Blockchain technology, while still in its infancy, is maturing rapidly. As it is growing, so does its userbase and the abundance and variety of available applications. However, scaling problems are an issue when the amount of data passing hits a limitation due to the insufficient capacities of the blockchain.

Layer 1 blockchain solutions help to improve the base protocols by changing their way of processing data. For example, the Ethereum network is now moving to a proof-of-stake (PoS) consensus algorithm. This new method of mining supports faster transaction speeds and more efficient energy use in the mining process. Sharding is another layer 1 scaling solution that breaks down authenticating and validating transactions into smaller pieces. Layer 2 scaling solutions increase throughput without tampering with any of the original decentralized or security characteristics integral to the original blockchain. Sidechains are blockchains linked to the main chain with a two-way peg. Parachains are chains that run parallel to one another in a system of interconnected blockchains. Sidechains and parachains are created within the same framework, with the same security attributes, and connected to the central relay chain. However, they can all also act independently to address their specific applications. ML-backed Layer 1 and 2 solutions could play a crucial role in orchestrating the interoperability between different networks and chains and help to optimize the scaling process of various blockchains.

On to the applications layer, blockchain technology could do more than document transactions. Smart contracts work by following simple “if–then” statements written into code on a blockchain. A network of computers executes the actions when predetermined conditions have been met and verified. Decentralized finance (DeFi) and decentralized apps (DApps) are emerging application areas that could benefit from ML.

ML could also be applied in more traditional crypto-finance tasks: automatic trading (trading bots), transaction fee optimization, and asset price prediction, including non-fungible tokens (NFT), among others.

Finally, ML could be employed to tackle security and privacy issues explicitly related to the cryptoverse: blockchain attacks (Sybil, Race, etc.), wallet and transaction anonymization/deanonymization, or other privacy concerns.

Dr. Georgios Siolas
Dr. Georgios Alexandridis
Dr. Paraskevi Tzouveli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Machine learning
  • Distributed ledger
  • Blockchain
  • Scaling
  • Sharding
  • Sidechain
  • Parachain
  • Network Interoperability
  • DeFi
  • DApps
  • Cryptofinance
  • Automatic trading
  • Transaction fee optimization
  • Asset price prediction
  • NFT
  • Security
  • Privacy

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Published Papers (6 papers)

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Research

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22 pages, 516 KiB  
Article
The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study
by Izdehar M. Aldyaflah, Wenbing Zhao, Shunkun Yang and Xiong Luo
Information 2024, 15(6), 302; https://doi.org/10.3390/info15060302 - 24 May 2024
Cited by 1 | Viewed by 864
Abstract
Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the [...] Read more.
Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the smart contracts for analysis have also been proposed. However, the impact of these input methods has not been systematically studied, which is the primary goal of this paper. In this preliminary study, we experimented with four common types of input, including Word2Vec, FastText, Bag-of-Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). To focus on the comparison of these input types, we used the same deep-learning model, i.e., convolutional neural networks, in all experiments. Using a public dataset, we compared the vulnerability detection performance of the four input types both in the binary classification scenarios and the multiclass classification scenario. Our findings show that TF-IDF is the best overall input type among the four. TF-IDF has excellent detection performance in all scenarios: (1) it has the best F1 score and accuracy in binary classifications for all vulnerability types except for the delegate vulnerability where TF-IDF comes in a close second, and (2) it comes in a very close second behind BoW (within 0.8%) in the multiclass classification. Full article
(This article belongs to the Special Issue Machine Learning for the Blockchain)
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25 pages, 2717 KiB  
Article
Machine Learning and Blockchain: A Bibliometric Study on Security and Privacy
by Alejandro Valencia-Arias, Juan David González-Ruiz, Lilian Verde Flores, Luis Vega-Mori, Paula Rodríguez-Correa and Gustavo Sánchez Santos
Information 2024, 15(1), 65; https://doi.org/10.3390/info15010065 - 22 Jan 2024
Cited by 5 | Viewed by 5445
Abstract
Machine learning and blockchain technology are fast-developing fields with implications for multiple sectors. Both have attracted a lot of interest and show promise in security, IoT, 5G/6G networks, artificial intelligence, and more. However, challenges remain in the scientific literature, so the aim is [...] Read more.
Machine learning and blockchain technology are fast-developing fields with implications for multiple sectors. Both have attracted a lot of interest and show promise in security, IoT, 5G/6G networks, artificial intelligence, and more. However, challenges remain in the scientific literature, so the aim is to investigate research trends around the use of machine learning in blockchain. A bibliometric analysis is proposed based on the PRISMA-2020 parameters in the Scopus and Web of Science databases. An objective analysis of the most productive and highly cited authors, journals, and countries is conducted. Additionally, a thorough analysis of keyword validity and importance is performed, along with a review of the most significant topics by year of publication. Co-occurrence networks are generated to identify the most crucial research clusters in the field. Finally, a research agenda is proposed to highlight future topics with great potential. This study reveals a growing interest in machine learning and blockchain. Topics are evolving towards IoT and smart contracts. Emerging keywords include cloud computing, intrusion detection, and distributed learning. The United States, Australia, and India are leading the research. The research proposes an agenda to explore new applications and foster collaboration between researchers and countries in this interdisciplinary field. Full article
(This article belongs to the Special Issue Machine Learning for the Blockchain)
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33 pages, 4029 KiB  
Article
A Secure and Privacy-Preserving Blockchain-Based XAI-Justice System
by Konstantinos Demertzis, Konstantinos Rantos, Lykourgos Magafas, Charalabos Skianis and Lazaros Iliadis
Information 2023, 14(9), 477; https://doi.org/10.3390/info14090477 - 28 Aug 2023
Cited by 1 | Viewed by 2938
Abstract
Pursuing “intelligent justice” necessitates an impartial, productive, and technologically driven methodology for judicial determinations. This scholarly composition proposes a framework that harnesses Artificial Intelligence (AI) innovations such as Natural Language Processing (NLP), ChatGPT, ontological alignment, and the semantic web, in conjunction with blockchain [...] Read more.
Pursuing “intelligent justice” necessitates an impartial, productive, and technologically driven methodology for judicial determinations. This scholarly composition proposes a framework that harnesses Artificial Intelligence (AI) innovations such as Natural Language Processing (NLP), ChatGPT, ontological alignment, and the semantic web, in conjunction with blockchain and privacy techniques, to examine, deduce, and proffer recommendations for the administration of justice. Specifically, through the integration of blockchain technology, the system affords a secure and transparent infrastructure for the management of legal documentation and transactions while preserving data confidentiality. Privacy approaches, including differential privacy and homomorphic encryption techniques, are further employed to safeguard sensitive data and uphold discretion. The advantages of the suggested framework encompass heightened efficiency and expediency, diminished error propensity, a more uniform approach to judicial determinations, and augmented security and privacy. Additionally, by utilizing explainable AI methodologies, the ethical and legal ramifications of deploying intelligent algorithms and blockchain technologies within the legal domain are scrupulously contemplated, ensuring a secure, efficient, and transparent justice system that concurrently protects sensitive information upholds privacy. Full article
(This article belongs to the Special Issue Machine Learning for the Blockchain)
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20 pages, 3201 KiB  
Article
Towards a Unified Architecture Powering Scalable Learning Models with IoT Data Streams, Blockchain, and Open Data
by Olivier Debauche, Jean Bertin Nkamla Penka, Moad Hani, Adriano Guttadauria, Rachida Ait Abdelouahid, Kaouther Gasmi, Ouafae Ben Hardouz, Frédéric Lebeau, Jérôme Bindelle, Hélène Soyeurt, Nicolas Gengler, Pierre Manneback, Mohammed Benjelloun and Carlo Bertozzi
Information 2023, 14(6), 345; https://doi.org/10.3390/info14060345 - 17 Jun 2023
Cited by 5 | Viewed by 2665
Abstract
The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and [...] Read more.
The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms. Full article
(This article belongs to the Special Issue Machine Learning for the Blockchain)
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20 pages, 1704 KiB  
Article
Scams and Solutions in Cryptocurrencies—A Survey Analyzing Existing Machine Learning Models
by Lakshmi Priya Krishnan, Iman Vakilinia, Sandeep Reddivari and Sanjay Ahuja
Information 2023, 14(3), 171; https://doi.org/10.3390/info14030171 - 8 Mar 2023
Cited by 7 | Viewed by 4553
Abstract
With the emergence of cryptocurrencies and Blockchain technology, the financial sector is turning its gaze toward this latest wave. The use of cryptocurrencies is becoming very common for multiple services. Food chains, network service providers, tech companies, grocery stores, and so many other [...] Read more.
With the emergence of cryptocurrencies and Blockchain technology, the financial sector is turning its gaze toward this latest wave. The use of cryptocurrencies is becoming very common for multiple services. Food chains, network service providers, tech companies, grocery stores, and so many other services accept cryptocurrency as a mode of payment and give several incentives for people who pay using them. Despite this tremendous success, cryptocurrencies have opened the door to fraudulent activities such as Ponzi schemes, HYIPs (high-yield investment programs), money laundering, and much more, which has led to the loss of several millions of dollars. Over the decade, solutions using several machine learning algorithms have been proposed to detect these felonious activities. The objective of this paper is to survey these models, the datasets used, and the underlying technology. This study will identify highly efficient models, evaluate their performances, and compile the extracted features, which can serve as a benchmark for future research. Fraudulent activities and their characteristics have been exposed in this survey. We have identified the gaps in the existing models and propose improvement ideas that can detect scams early. Full article
(This article belongs to the Special Issue Machine Learning for the Blockchain)
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Review

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19 pages, 1488 KiB  
Review
Blockchain and Machine Learning: A Critical Review on Security
by Hamed Taherdoost
Information 2023, 14(5), 295; https://doi.org/10.3390/info14050295 - 17 May 2023
Cited by 19 | Viewed by 10086
Abstract
Blockchain is the foundation of all cryptocurrencies, while machine learning (ML) is one of the most popular technologies with a wide range of possibilities. Blockchain may be improved and made more effective by using ML. Even though blockchain technology uses encryption to safeguard [...] Read more.
Blockchain is the foundation of all cryptocurrencies, while machine learning (ML) is one of the most popular technologies with a wide range of possibilities. Blockchain may be improved and made more effective by using ML. Even though blockchain technology uses encryption to safeguard data, it is not completely reliable. Various elements, including the particular use case, the type of data, and legal constraints can determine whether it is suitable for keeping private and sensitive data. While there may be benefits, it is important to take into account possible hazards and abide by privacy and security laws. The blockchain itself is secure, but additional applications and layers are not. In terms of security, ML can aid in the development of blockchain applications. Therefore, a critical investigation is required to better understand the function of ML and blockchain in enhancing security. This study examines the current situation, evaluates the articles it contains, and presents an overview of the security issues. Despite their existing limitations, the papers included from 2012 to 2022 highlighted the importance of ML’s impact on blockchain security. ML and blockchain can enhance security, but challenges remain; advances such as federated learning and zero-knowledge proofs are important, and future research should focus on privacy and integration with other technologies. Full article
(This article belongs to the Special Issue Machine Learning for the Blockchain)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Cryptocurrency price prediction based on deep learning
Author: Tzouveli
Highlights: Bitcoin, Blockchain, Cryptocurrency, Time Series Forecasting, Deep Learning, Recurrent Neural Networks

Title: Blockchain-Based Deep Learning to Process IoT Data Acquisition in Smart Aeroponics Systems
Authors: Rachida Ait Abdelouahid; Olivier Debauche; Abdelaziz Marzak; Frédéric Lebeau
Affiliation: Hassan II University - Casablanca, Faculty of sciences Ben M’sik, LTIM
Abstract: Aeroponics is a new technology for growing plants outside of the substrate and is particularly well suited to growing plants in zero gravity. Aeroponics is a very sensitive growing method because the roots are in the open air, which requires decisions to be made based on verified data and in a rapid manner because the plants, being out of the substrate, die in a few tens of minutes in the event of a breakdown or in the event of late detection of disease. The criticality of this mode of cultivation requires the distribution and security of sabotage control systems. IoT, AI, and blockchain are new technologies that in combination allow new secure approaches to data collection and decision-making. The Internet of Things allows us to measure the environmental data of plants and to act on the environment in which it evolves. The blockchain allows to the storage of data in a chronological and unforgeable way while artificial intelligence allows prediction, detection, and making decisions based on the data stored in the blockchain. In this paper, we develop an architecture based on the coupling of IoT, machine learning, and blockchain for the strategic control of aeroponics-based vital food production systems.

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