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Future Internet
Special Issues
Machine Learning for Blockchain and IoT System in Smart Cities
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Machine Learning for Blockchain and IoT System in Smart Cities

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 1 June 2025 | Viewed by 5906

Special Issue Editors

Dr. José A. Afonso

E-Mail Website
Guest Editor
1. CMEMS—UMinho, University of Minho, 4800-058 Guimarães, Portugal
2. LABBELS—Associate Laboratory, University of Minho, 4710-057 Braga, Portugal
3. Department of Industrial Electronics, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal
Interests: internet of things; wireless sensor networks; body sensor network; mobile phone sensing; quality of service; medium access control
Special Issues, Collections and Topics in MDPI journals
Dr. Joao Ferreira

E-Mail Website
Guest Editor
Department of Information Sciences, Technologies and Architecture, University Institute of Lisbon, 1649-026 Lisbon, Portugal
Interests: electric vehicle; data science; IoT; blockchain; artificial intelligence; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A wide range of smart city applications has been emerging as a result of developments in digital technologies like the Internet of Things (IoT), fog/edge/cloud computing, and cyber-physical systems. IoT applications may benefit from the enormous contributions of recent advancements in artificial intelligence-based technologies and methodologies, such as machine learning and deep learning, which are used to extract the correct information from large amounts of data.

Furthermore, the rapid uptake of blockchain technology plays a critical role in the creation of a new ecosystem for digital smart cities. In order to create sustainable ecosystems for IoT applications, artificial intelligence and blockchain technology convergence have the potential to enhance smart city infrastructures. These scientific and technological advances also present opportunities and challenges for creating viable Internet of Things applications.

This Special Issue aims to discuss trends and highlight research advances in the fields of the Internet of Things, blockchain technology and artificial intelligence.

Dr. Jose A. Afonso
Dr. Joao Carlos Amaro Ferreira
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. Future Internet 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

  • internet of things
  • artificial intelligence
  • machine learning
  • deep learning
  • blockchain
  • smart sensors
  • wireless sensor networks
  • decision support systems
  • communications for smart cities

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Published Papers (1 paper)

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Research

30 pages, 4220 KiB  
Article
Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms
by Ashish Koirala, Rabindra Bista and Joao C. Ferreira
Future Internet 2023, 15(6), 210; https://doi.org/10.3390/fi15060210 - 9 Jun 2023
Cited by 4 | Viewed by 4413
Abstract
The Internet of Things (IoT) shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones. Contrarily, storing and operating information and maintaining its confidentiality and security is a concerning issue in the IoT. Throughout the whole operational [...] Read more.
The Internet of Things (IoT) shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones. Contrarily, storing and operating information and maintaining its confidentiality and security is a concerning issue in the IoT. Throughout the whole operational process, considering transparency in its privacy, data protection, and disaster recovery, it needs state-of-the-art systems and methods to tackle the evolving environment. This research aims to improve the security of IoT devices by investigating the likelihood of network attacks utilizing ordinary device network data and attack network data acquired from similar statistics. To achieve this, IoT devices dedicated to smart healthcare systems were utilized, and botnet attacks were conducted on them for data generation. The collected data were then analyzed using statistical measures, such as the Pearson coefficient and entropy, to extract relevant features. Machine learning algorithms were implemented to categorize normal and attack traffic with data preprocessing techniques to increase accuracy. One of the most popular datasets, known as BoT-IoT, was cross-evaluated with the generated dataset for authentication of the generated dataset. The research provides insight into the architecture of IoT devices, the behavior of normal and attack networks on these devices, and the prospects of machine learning approaches to improve IoT device security. Overall, the study adds to the growing body of knowledge on IoT device security and emphasizes the significance of adopting sophisticated strategies for detecting and mitigating network attacks. Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT System in Smart Cities)
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