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Future Internet
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Security and Privacy in Blockchains and the IoT III
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Security and Privacy in Blockchains and the IoT III

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4088

Special Issue Editors

Dr. Christoph Stach

E-Mail Website
Guest Editor
Department of Applications of Parallel and Distributed Systems, Institute for Parallel and Distributed Systems, University of Stuttgart, D-70569 Stuttgart, Germany
Interests: trustworthy data science and analytics; security techniques for the Internet of Things; secure data management; privacy-aware smart services; privacy-aware big data processing; privacy-aware machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Clémentine Gritti

E-Mail Website
Guest Editor
Digital Security Department, EURECOM, Campus SophiaTech, 450 Route des Chappes, 06904 Biot Sophia Antipolis Cedex, France
Interests: applied cryptography; data and network security; blockchain technologies; post-quantum cryptography
Special Issues, Collections and Topics in MDPI journals
Dr. Iouliana Litou

E-Mail Website
Guest Editor
Department of Informatics, Athens University of Economics and Business, 104 34 Athens, Greece
Interests: social media; influence maximization; graph analytics; recommender systems; privacy; data and network security; secure AI; generative AI

Special Issue Information

Dear Colleagues,

In recent years, the emergence of blockchain technologies and the Internet of Things (IoT) has shaped a dynamic and innovative landscape that has the potential to leave a lasting mark on our digital society. Blockchains, originally developed as a means to secure decentralized digital currencies, have now outgrown their original purpose and transformed a wide range of application areas, while the IoT has enabled a multitude of devices and sensors to connect, collaborate, and share data. These disruptive technologies have already impacted our daily lives, businesses, and society as a whole, introducing a plethora of novel opportunities and challenges.

Amid this profound digital transformation, the need for ensuring security and privacy within blockchain and IoT ecosystems has become increasingly evident. The synergy between these two technologies is promising yet also entails multiple security and privacy issues that require comprehensive research. Resilient data and transaction security, protection of sensitive information, and preservation of user privacy are essential aspects of a sustainable and trustworthy blockchain and IoT infrastructure. In addition, trust in both the underlying technologies and the data they process is also a key factor in this context.

In this regard, trust in technology, particularly in the security and reliability of blockchain and IoT systems, is fundamental to their widespread adoption and use. Users must have confidence in the resilience of these systems to cyber threats and the integrity of the data they process. Trust in data concerns the veracity and authenticity of information provided by these systems. As the data collected and processed by IoT devices often form the foundation for decisions in a variety of areas, from healthcare to supply chain management, ensuring the trustworthiness of these data is therefore of paramount importance.

With the two successful previous volumes of this Special Issue having already covered a wide range of security and privacy aspects in blockchain and IoT environments, this third volume also aims to spotlight trust issues and the correlations between these three topics. We seek to bring together researchers, experts, and practitioners in the fields of security, privacy, and trust to foster multidisciplinary approaches to solving the pressing resilience problems regarding the application of blockchain and IoT technologies. Within this context, we invite submissions of high-quality research papers, comprehensive review articles, and insightful case studies. Our goal is to provide a platform for sharing innovative approaches, best practices, and insightful lessons learned.

Dr. Christoph Stach
Dr. Clémentine Gritti
Dr. Iouliana Litou
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

  • security and privacy solutions for blockchain and IoT systems
  • authentication and access control in blockchain and IoT environments
  • privacy-preserving smart contracts and consensus mechanisms
  • privacy-preserving data collection
  • threat detection and prevention in blockchain and IoT applications
  • secure data sharing and storage in IoT using blockchain technologies
  • scalability and efficiency of blockchain-based data management
  • trust-building measures in the security and privacy of blockchain and IoT systems
  • case studies on security and privacy challenges in real-world applications
  • regulatory and legal aspects of security and privacy in these domains

Related Special Issues

Published Papers (3 papers)

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Research

22 pages, 2021 KiB  
Article
HSM4SSL: Leveraging HSMs for Enhanced Intra-Domain Security
by Yazan Aref and Abdelkader Ouda
Future Internet 2024, 16(5), 148; https://doi.org/10.3390/fi16050148 - 26 Apr 2024
Viewed by 231
Abstract
In a world where digitization is rapidly advancing, the security and privacy of intra-domain communication within organizations are of critical concern. The imperative to secure communication channels among physical systems has led to the deployment of various security approaches aimed at fortifying networking [...] Read more.
In a world where digitization is rapidly advancing, the security and privacy of intra-domain communication within organizations are of critical concern. The imperative to secure communication channels among physical systems has led to the deployment of various security approaches aimed at fortifying networking protocols. However, these approaches have typically been designed to secure protocols individually, lacking a holistic perspective on the broader challenge of intra-domain communication security. This omission raises fundamental concerns about the safety and integrity of intra-domain environments, where all communication occurs within a single domain. As a result, this paper introduces HSM4SSL, a comprehensive solution designed to address the evolving challenges of secure data transmission in intra-domain environments. By leveraging hardware security modules (HSMs), HSM4SSL aims to utilize the Secure Socket Layer (SSL) protocol within intra-domain environments to ensure data confidentiality, authentication, and integrity. In addition, solutions proposed by academic researchers and in the industry have not addressed the issue in a holistic and integrative manner, as they only apply to specific types of environments or servers and do not utilize all cryptographic operations for robust security. Thus, HSM4SSL bridges this gap by offering a unified and comprehensive solution that includes certificate management, key management practices, and various security services. HSM4SSL comprises three layers to provide a standardized interaction between software applications and HSMs. A performance evaluation was conducted comparing HSM4SSL with a benchmark tool for cryptographic operations. The results indicate that HSM4SSL achieved 33% higher requests per second (RPS) compared to OpenSSL, along with a 13% lower latency rate. Additionally, HSM4SSL efficiently utilizes CPU and network resources, outperforming OpenSSL in various aspects. These findings highlight the effectiveness and reliability of HSM4SSL in providing secure communication within intra-domain environments, thus addressing the pressing need for enhanced security mechanisms. Full article
(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT III)
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38 pages, 10789 KiB  
Article
Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection
by Dominic Lightbody, Duc-Minh Ngo, Andriy Temko, Colin C. Murphy and Emanuel Popovici
Future Internet 2024, 16(3), 88; https://doi.org/10.3390/fi16030088 - 05 Mar 2024
Viewed by 1209
Abstract
The growth of the Internet of Things (IoT) has led to a significant rise in cyber attacks and an expanded attack surface for the average consumer. In order to protect consumers and infrastructure, research into detecting malicious IoT activity must be of the [...] Read more.
The growth of the Internet of Things (IoT) has led to a significant rise in cyber attacks and an expanded attack surface for the average consumer. In order to protect consumers and infrastructure, research into detecting malicious IoT activity must be of the highest priority. Security research in this area has two key issues: the lack of datasets for training artificial intelligence (AI)-based intrusion detection models and the fact that most existing datasets concentrate only on one type of network traffic. Thus, this study introduces Dragon_Pi, an intrusion detection dataset designed for IoT devices based on side-channel power consumption data. Dragon_Pi comprises a collection of normal and under-attack power consumption traces from separate testbeds featuring a DragonBoard 410c and a Raspberry Pi. Dragon_Slice is trained on this dataset; it is an unsupervised convolutional autoencoder (CAE) trained exclusively on held-out normal slices from Dragon_Pi for anomaly detection. The Dragon_Slice network has two iterations in this study. The original achieves 0.78 AUC without post-processing and 0.876 AUC with post-processing. A second iteration of Dragon_Slice, utilising dropout to further impede the CAE’s ability to reconstruct anomalies, outperforms the original network with a raw AUC of 0.764 and a post-processed AUC of 0.89. Full article
(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT III)
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18 pages, 2860 KiB  
Article
Investigation of Phishing Susceptibility with Explainable Artificial Intelligence
by Zhengyang Fan, Wanru Li, Kathryn Blackmond Laskey and Kuo-Chu Chang
Future Internet 2024, 16(1), 31; https://doi.org/10.3390/fi16010031 - 17 Jan 2024
Viewed by 2168
Abstract
Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become [...] Read more.
Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become a prevalent method in the study of phishing susceptibility. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine-learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. The machine learning model yielded an accuracy of 78%, with a recall of 71%, and a precision of 57%. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual’s susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual. Full article
(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT III)
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