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Electronics
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Futuristic Security and Privacy in 6G-Enabled IoT
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Futuristic Security and Privacy in 6G-Enabled IoT

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 1 July 2024 | Viewed by 7863

Special Issue Editors

Dr. Victor R. Kebande

E-Mail Website
Guest Editor
Department of Computer Science (DIDA), Blekinge Institute of Technology, 37179 Karlskrona, Sweden
Interests: cyber; information security and digital forensics in the IoT (IoT & IIoT security); digital forensics-incident response; cyber-physical system protection; critical infrastructure protection; cloud computing security; computer systems; distributed system security; threat hunting; modeling and cyber-security risk assessment; blockchain technologies; privacy-preserving techniques; cybersecurity of renewable energy
Dr. Sadi Alawadi

E-Mail Website
Guest Editor
Center for Applied Intelligent Systems Research, School of Information Technology, Halmstad University, 30118 Halmstad, Sweden
Interests: Internet of Things (IoT); machine learning (ML); deep learning; real-time analysis; data visualizations; big data; digital forensics; edge and cloud computing; dimensionality reduction; blockchain; federated learning; transfer and interactive learning; internet of health
Dr. Fahed Alkhabbas

E-Mail Website
Guest Editor
Internet of Things and People Research Center, Department of Computer Science and Media Technology, Malmö University, 20506 Malmö, Sweden
Interests: software engineering; Internet of Things; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is currently witnessing a shift in how communication and data are being handled; this can be attributed to improved communication techniques, increased device usage, and the need to make meaning of the data being exchanged on a daily basis. Sixth-generation (6G) networks are projected to transform communication and networks in terms of frequency, bandwidth, data rate, and spectral efficiency. Consequently, the Internet of Things (IoT) has been at the center of all these transformations, where it has recently gained significant attention. While we focus on the effects of this connectedness, it is important to highlight that sixth-generation (6G) technologies are poised to revolutionize how telecommunications is conducted. In this Special Issue, we will look at the effect that the introduction of 6G will have on continued IoT proliferation. Security and privacy in the IoT have been hotly contested topics in the last decade; however, with the introduction of 6G, it is expected that challenges, benefits, and new achievements will be witnessed not only in how devices connect, but also in how communication is handled, with the interoperability, efficiency, safety, security and privacy of devices and data with intelligent attributes being relevant aspects. Given this context, in this Special Issue we welcome researchers from academia and industry to submit cutting-edge articles on relevant topics that offer pertinent solutions to open problems and existing, or future, challenges in IoT infrastructures.

This Special Issue offers an opportunity for researchers and practitioners to explore the interplay of security and privacy with 6G and IoT, as well as current state-of-the-art, theoretical, technical, and futuristic security and privacy with the sporadic developments in 6G communication networks in IoT.

We solicit papers in the following areas:

  • Security and privacy in 6G and IoT;
  • Evolutionary intelligence in 6G;
  • Securing 6G in IoT ecosystems;
  • Privacy, trust, and reputation in 6G networks;
  • Privacy of IoT devices;
  • Security of smart ecosystems;
  • Machine learning in IoT systems;
  • Anomaly detection in IoT environment;
  • Threat and vulnerability detection in IoT environment;
  • Artificial intelligence in IoT;
  • Offensive security in IoT infrastructure;
  • Blockchain in 6G-enabled IoT environments;
  • Blockchain for renewable energy in IoT environments;
  • Edge and fog protection techniques in IoT;
  • Securing the cloud;
  • Interactive and federated learning techniques in IoT;
  • Information security;
  • Advances in IoT and IIoT;
  • Advances in intelligent forensics in IoT;
  • 6G fabric, open problems, and future solutions.

Dr. Victor R. Kebande
Dr. Sadi Alawadi
Dr. Fahed Alkhabbas
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • future security
  • privacy
  • IoT
  • 6G
  • reputation in 6G
  • anomaly detection in IoT
  • renewable energy in IoT
  • blockchain and IoT
  • threat and vulnerability in IoT

Published Papers (3 papers)

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Research

44 pages, 1561 KiB  
Article
A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions
by Hussam N. Fakhouri, Sadi Alawadi, Feras M. Awaysheh, Imad Bani Hani, Mohannad Alkhalaileh and Faten Hamad
Electronics 2023, 12(22), 4604; https://doi.org/10.3390/electronics12224604 - 10 Nov 2023
Cited by 2 | Viewed by 2536
Abstract
Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) [...] Read more.
Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms’ role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network’s security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network’s underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security. Full article
(This article belongs to the Special Issue Futuristic Security and Privacy in 6G-Enabled IoT)
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28 pages, 3803 KiB  
Article
Anomaly Detection in 6G Networks Using Machine Learning Methods
by Mamoon M. Saeed, Rashid A. Saeed, Maha Abdelhaq, Raed Alsaqour, Mohammad Kamrul Hasan and Rania A. Mokhtar
Electronics 2023, 12(15), 3300; https://doi.org/10.3390/electronics12153300 - 31 Jul 2023
Cited by 11 | Viewed by 2662
Abstract
While the cloudification of networks with a micro-services-oriented design is a well-known feature of 5G, the 6G era of networks is closely related to intelligent network orchestration and management. Consequently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have a big [...] Read more.
While the cloudification of networks with a micro-services-oriented design is a well-known feature of 5G, the 6G era of networks is closely related to intelligent network orchestration and management. Consequently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have a big part to play in the 6G paradigm that is being imagined. Future end-to-end automation of networks requires proactive threat detection, the use of clever mitigation strategies, and confirmation that 6G networks will be self-sustaining. To strengthen and consolidate the role of AI in safeguarding 6G networks, this article explores how AI may be employed in 6G security. In order to achieve this, a novel anomaly detection system for 6G networks (AD6GNs) based on ensemble learning (EL) for communication networks was redeveloped in this study. The first stage in the EL-ADCN process is pre-processing. The second stage is the feature selection approach. It applies the reimplemented hybrid approach using a comparison of the ensemble learning and feature selection random forest algorithms (CFS-RF). NB2015, CIC_IDS2017, NSL KDD, and CICDDOS2019 are the three datasets, each given a reduced dimensionality, and the top subset characteristic for each is determined separately. Hybrid EL techniques are used in the third step to find intrusions. The average voting methodology is employed as an aggregation method, and two classifiers—support vector machines (SVM) and random forests (RF)—are modified to be used as EL algorithms for bagging and adaboosting, respectively. Testing the concept of the last step involves employing classification forms that are binary and multi-class. The best experimental results were obtained by applying 30, 35, 40, and 40 features of the reimplemented system to the three datasets: NSL_KDD, UNSW_NB2015, CIC_IDS2017, and CICDDOS2019. For the NSL_KDD dataset, the accuracy was 99.5% with a false alarm rate of 0.0038; the accuracy was 99.9% for the UNSW_NB2015 dataset with a false alarm rate of 0.0076; and the accuracy was 99.8% for the CIC_IDS2017 dataset with a false alarm rate of 0.0009. However, the accuracy was 99.95426% for the CICDDOS2019 dataset, with a false alarm rate of 0.00113. Full article
(This article belongs to the Special Issue Futuristic Security and Privacy in 6G-Enabled IoT)
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25 pages, 11333 KiB  
Article
Non-Pattern-Based Anomaly Detection in Time-Series
by Volodymyr Tkach, Anton Kudin, Victor R. Kebande, Oleksii Baranovskyi and Ivan Kudin
Electronics 2023, 12(3), 721; https://doi.org/10.3390/electronics12030721 - 1 Feb 2023
Cited by 1 | Viewed by 2006
Abstract
Anomaly detection across critical infrastructures is not only a key step towards detecting threats but also gives early warnings of the likelihood of potential cyber-attacks, faults, or infrastructure failures. Owing to the heterogeneity and complexity of the cybersecurity field, several anomaly detection algorithms [...] Read more.
Anomaly detection across critical infrastructures is not only a key step towards detecting threats but also gives early warnings of the likelihood of potential cyber-attacks, faults, or infrastructure failures. Owing to the heterogeneity and complexity of the cybersecurity field, several anomaly detection algorithms have been suggested in the recent past based on the literature; however, there still exists little or no research that points or focuses on Non-Pattern Anomaly Detection (NP-AD) in Time-Series at the time of writing this paper. Most of the existing anomaly detection approaches refer to the initial profiling, i.e., defining which behavior represented by time series is “normal”, whereas everything that does not meet the criteria of “normality” is set as “abnormal” or anomalous. Such a definition does not reflect the complexity and sophistication of anomaly nature. Under different conditions, the same behavior may or may not be anomalous. Therefore, the authors of this paper posit the need for NP-AD in Time-Series as a step toward showing the relevance of deviating or not conforming to expected behaviors. Non-Pattern (NP), in the context of this paper, illustrates non-conforming patterns or a technique of deviating with respect to some characteristics while dynamically adapting to changes. Based on the experiments that have been conducted in this paper, it has been observed that the likelihood of NP-AD in Time-Series is a significant approach based on the margins of data streams that have been used from the perspective of non-seasonal time series with outliers, the Numenta Anomaly Benchmark (NAB) dataset and the SIEM SPLUNK machine learning toolkit. It is the authors’ opinion that this approach provides a significant step toward predicting futuristic anomalies across diverse cyber, critical infrastructures, and other complex settings. Full article
(This article belongs to the Special Issue Futuristic Security and Privacy in 6G-Enabled IoT)
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