Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
3.4
Journals
Future Internet
Special Issues
Security in the Internet of Things (IoT)
share announcement

Security in the Internet of Things (IoT)

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 4903

Special Issue Editors

Dr. Javier Prieto

E-Mail Website
Guest Editor
BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
Interests: artificial intelligence; blockchain; deep learning; satellite systems; robot vision; cognitive robotics; sensor fusion; data fusion; mobile robotics; wireless networks; robotics; security; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Ricardo Alonso

E-Mail Website
Guest Editor
1. AIR Institute, Deep Tech Lab, Paseo de Belén 9A, 47011 Valladolid, Spain
2. BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
3. Higher School of Engineering and Technology, International University of La Rioja (UNIR), 26006 Logroño, Spain
Interests: Internet of Things; edge computing; distributed ledger and blockchain technologies; embedded systems; indoor location systems; cloud computing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The number of new Internet-connected devices is growing year on year. While connections from non-IoT devices have been increasing moderately in recent years, connections from IoT devices are growing at a much faster rate, driven especially by 5G IoT, low-power wireless area networks (LP-WANs) and wireless personal area networks (WPANs). IoT now represents a key digital enabling technology in sectors, such as Industry 4.0/5.0, smart cities and smart territories, e-healthcare, e-entertainment, smart homes and buildings, and agriculture. However, one of the main challenges facing IoT solutions is cybersecurity. IoT devices often have reduced computational capabilities (e.g., CPU, RAM) to minimize their size and power consumption, which makes it difficult for them to encrypt information using traditional protocols or the ability to run intelligent processes or agents to combat cyber-attacks or protect the privacy of user data. In fact, one of the main cybersecurity issues in the IoT domain is the identification of the devices that connect to the networks. In this sense, it is necessary to investigate and validate new technologies and techniques aimed at dealing with the different cybersecurity issues that may appear in IoT scenarios.

For this purpose, this Special Issue will be focusing upon, but not limited to the following topics:

  • Novel cybersecurity architectures, protocols and algorithms in IoT and Edge-IoT scenarios;
  • Distributed and collaborative knowledge and data privacy management;
  • Security information and event management (SIEM) in IoT scenarios;
  • Cyber threat intelligence modelling and identification frameworks in IoT;
  • Security and privacy frameworks for transferring data and models in Edge-IoT scenarios;
  • Trustworthy Artificial Intelligence applied to cybersecurity and data privacy in IoT scenarios;
  • Imitation learning and reinforcement learning for cyberattack detection and mitigation;
  • Multi-agent systems and virtual agent organizations for cybersecurity in IoT scenarios;
  • Federated learning and federated reinforcement learning for data privacy;
  • Deep Learning and Deep Reinforcement Learning at the Edge for cybersecurity in IoT and Industrial IoT;
  • Intelligent algorithms to manage software-defined networks and network function virtualization in Edge-IoT scenarios.

Dr. Javier Prieto
Dr. Ricardo Alonso
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
  • cybersecurity
  • edge computing
  • artificial intelligence
  • federated learning
  • data privacy

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 980 KiB  
Article
Implementing Federated Governance in Data Mesh Architecture
by Anton Dolhopolov, Arnaud Castelltort and Anne Laurent
Future Internet 2024, 16(4), 115; https://doi.org/10.3390/fi16040115 - 29 Mar 2024
Viewed by 506
Abstract
Analytical data platforms have been used for decades to improve organizational performance. Starting from the data warehouses used primarily for structured data processing, through the data lakes oriented for raw data storage and post-hoc data analyses, to the data lakehouses—a combination of raw [...] Read more.
Analytical data platforms have been used for decades to improve organizational performance. Starting from the data warehouses used primarily for structured data processing, through the data lakes oriented for raw data storage and post-hoc data analyses, to the data lakehouses—a combination of raw storage and business intelligence pre-processing for improving the platform’s efficacy. But in recent years, a new architecture called Data Mesh has emerged. The main promise of this architecture is to remove the barriers between operational and analytical teams in order to boost the overall value extraction from the big data. A number of attempts have been made to formalize and implement it in existing projects. Although being defined as a socio-technical paradigm, data mesh still lacks the technology support to enable its widespread adoption. To overcome this limitation, we propose a new view of the platform requirements alongside the formal governance definition that we believe can help in the successful adoption of the data mesh. It is based on fundamental aspects such as decentralized data domains and federated computational governance. In addition, we also present a blockchain-based implementation of a mesh platform as a practical validation of our theoretical proposal. Overall, this article demonstrates a novel research direction for information system decentralization technologies. Full article
(This article belongs to the Special Issue Security in the Internet of Things (IoT))
►▼ Show Figures

Figure 1

16 pages, 2560 KiB  
Article
Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data
by Konstantinos Psychogyios, Andreas Papadakis, Stavroula Bourou, Nikolaos Nikolaou, Apostolos Maniatis and Theodore Zahariadis
Future Internet 2024, 16(3), 73; https://doi.org/10.3390/fi16030073 - 23 Feb 2024
Viewed by 1305
Abstract
The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential [...] Read more.
The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential data, obstruct activity, etc. To this end, intrusion detection systems (IDSs) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparisons with previous attacks. However, with the increased availability of computational power and data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time series format and use predictive models to forecast forthcoming malign packets. We propose a new architecture combining convolutional neural networks, long short-term memory networks, and attention. The findings indicate that our model performs strongly, exhibiting an F1 score and AUC that are within margins of 1% and 3%, respectively, when compared to conventional real-time detection. Also, our architecture achieves an ∼8% F1 score improvement compared to an LSTM (long short-term memory) model. Full article
(This article belongs to the Special Issue Security in the Internet of Things (IoT))
►▼ Show Figures

Figure 1

19 pages, 2594 KiB  
Article
Detection of Man-in-the-Middle (MitM) Cyber-Attacks in Oil and Gas Process Control Networks Using Machine Learning Algorithms
by Ugochukwu Onyekachi Obonna, Felix Kelechi Opara, Christian Chidiebere Mbaocha, Jude-Kennedy Chibuzo Obichere, Isdore Onyema Akwukwaegbu, Miriam Mmesoma Amaefule and Cosmas Ifeanyi Nwakanma
Future Internet 2023, 15(8), 280; https://doi.org/10.3390/fi15080280 - 21 Aug 2023
Cited by 1 | Viewed by 1919
Abstract
Recently, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks. Examples include the denial-of-service (DoS), distributed denial-of-service (DDoS), and man-in-the-middle (MitM) attacks, and this may have largely been caused by the integration of open network to [...] Read more.
Recently, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks. Examples include the denial-of-service (DoS), distributed denial-of-service (DDoS), and man-in-the-middle (MitM) attacks, and this may have largely been caused by the integration of open network to operation technology (OT) as a result of low-cost network expansion. The connection of OT to the internet for firmware updates, third-party support, or the intervention of vendors has exposed the industry to attacks. The inability to detect these unpredictable cyber-attacks exposes the PCN, and a successful attack can lead to devastating effects. This paper reviews the different forms of cyber-attacks in PCN of oil and gas installations while proposing the use of machine learning algorithms to monitor data exchanges between the sensors, controllers, processes, and the final control elements on the network to detect anomalies in such data exchanges. Python 3.0 Libraries, Deep-Learning Toolkit, MATLAB, and Allen Bradley RSLogic 5000 PLC Emulator software were used in simulating the process control. The outcomes of the experiments show the reliability and functionality of the different machine learning algorithms in detecting these anomalies with significant precise attack detections identified using tree algorithms (bagged or coarse ) for man-in-the-middle (MitM) attacks while taking note of accuracy-computation complexity trade-offs. Full article
(This article belongs to the Special Issue Security in the Internet of Things (IoT))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop