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Applied Sciences
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Security in Cloud Computing, Big Data and Internet of Things
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Security in Cloud Computing, Big Data and Internet of Things

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 8548

Special Issue Editors

Dr. Ying Weng

E-Mail Website
Guest Editor
School of Computer Science, University of Nottingham, Ningbo 315100, China
Interests: big data on image/video processing; computer vision; machine learning; Internet of Things; multimedia security & forensics; security & QoS in wireless networks
Dr. Alexandro Baldassin

E-Mail Website
Guest Editor
Computer Science, Universidade Estadual Paulista (Unesp), 01049-010 São Paulo, Brazil
Interests: parallel computing; high performance computing; transactional memory; persistent memory
Prof. Dr. Kecheng Liu

E-Mail Website
Guest Editor
Henley Business School, University of Reading, Berkshire RG6 6UR, UK
Interests: big data; IoT and cloud computing in informatics and e-business
Dr. Zhuo Chen

E-Mail Website
Guest Editor
Department of Health Policy and Management, University of Georgia, Athens, GA 30609, USA
Interests: big data in health economics, health systems, and economic evaluation

Special Issue Information

Dear Colleagues,

The growing popularity and development of cloud computing, big data, and internet of things (IoT) technologies have rendered security a predominant topic of research. In the era characterised by digital connectivity and big data, it is crucial to process, manage, and use shared data effectively in a secure manner. Because IoT devices generate data, more smart objects and IoT applications mean more security challenges. Big data is processed, stored, and shared on cloud computing platforms. Cloud computing provides various elastic and scalable IT services, while also presenting privacy and security problems. Service models such as SaaS and PaaS require security at different levels of service. Additionally, modern cloud technologies based on distributed serverless architectures and ephemeral assets such as FaaS make security even more important. This Special Issue focuses on novel research addressing security issues in three main areas: cloud computing, big data, and the internet of things, from the perspectives of theoretical models and mechanisms, technology, and applications. Topics of interests include (but are not limited to):

  • Data security;
  • Data encryption;
  • Data leakage
  • Cloud protection;
  • Virtualization;
  • Broken authentication;
  • IoT secure communication;
  • Secure lifecycle management.

Dr. Ying Weng
Dr. Alexandro Baldassin
Prof. Dr. Kecheng Liu
Dr. Zhuo Chen
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. Applied Sciences 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.

Published Papers (5 papers)

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Research

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22 pages, 3977 KiB  
Article
Evolutionary Game Analysis of Copyright Protection for NFT Digital Works Considering Collusive Behavior
by Yudong Gao, Xuemei Xie and Yuan Ni
Appl. Sci. 2023, 13(20), 11261; https://doi.org/10.3390/app132011261 - 13 Oct 2023
Viewed by 842
Abstract
The non-fungible tokens trading of digital content works, as an emerging business model, has rapidly developed while also posing challenges to current copyright protection. The NFT infringement incidents in recent years have exposed many issues, such as lack of government regulation, imperfect copyright [...] Read more.
The non-fungible tokens trading of digital content works, as an emerging business model, has rapidly developed while also posing challenges to current copyright protection. The NFT infringement incidents in recent years have exposed many issues, such as lack of government regulation, imperfect copyright protection mechanisms, and illegal profits from service platforms. Considering the collusive behavior during the NFT minting process, this study uses evolutionary game theory to model a game composed of three populations: digital content creators; NFT service platforms; and government regulatory agencies. We derived and analyzed the replication dynamics of the game to determine the evolutionary stability strategy. In addition, combined with numerical simulations, we also analyzed the impact of individual factors on the stability of system evolution. This study identifies that the incentives and fines set by the government must be above a certain threshold in order for game results to develop toward an ideal equilibrium state, and the government can try to improve the efficiency of obtaining and updating market information and set dynamic punishment and reward mechanisms based on this. This study also found that excessive rewards are not conducive to the government fulfilling its own regulatory responsibilities. In this regard, the government can use information technology to reduce the cost of regulation, thereby partially offsetting the costs brought about by incentive mechanisms. In addition, the government can also enhance the governance participation of platforms and creators to improve the robustness of digital copyright protection by strengthening media construction and cultivating public copyright awareness. This study helps to understand the complex relationship between NFT service platforms, digital content creators, and government regulatory authorities and proves the practical meaning of countermeasures and suggestions for improving government digital copyright regulations. Full article
(This article belongs to the Special Issue Security in Cloud Computing, Big Data and Internet of Things)
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19 pages, 4073 KiB  
Article
Towards an Intelligent Intrusion Detection System to Detect Malicious Activities in Cloud Computing
by Hanaa Attou, Mouaad Mohy-eddine, Azidine Guezzaz, Said Benkirane, Mourade Azrour, Abdulatif Alabdultif and Naif Almusallam
Appl. Sci. 2023, 13(17), 9588; https://doi.org/10.3390/app13179588 - 24 Aug 2023
Cited by 7 | Viewed by 1543
Abstract
Several sectors have embraced Cloud Computing (CC) due to its inherent characteristics, such as scalability and flexibility. However, despite these advantages, security concerns remain a significant challenge for cloud providers. CC introduces new vulnerabilities, including unauthorized access, data breaches, and insider threats. The [...] Read more.
Several sectors have embraced Cloud Computing (CC) due to its inherent characteristics, such as scalability and flexibility. However, despite these advantages, security concerns remain a significant challenge for cloud providers. CC introduces new vulnerabilities, including unauthorized access, data breaches, and insider threats. The shared infrastructure of cloud systems makes them attractive targets for attackers. The integration of robust security mechanisms becomes crucial to address these security challenges. One such mechanism is an Intrusion Detection System (IDS), which is fundamental in safeguarding networks and cloud environments. An IDS monitors network traffic and system activities. In recent years, researchers have explored the use of Machine Learning (ML) and Deep Learning (DL) approaches to enhance the performance of IDS. ML and DL algorithms have demonstrated their ability to analyze large volumes of data and make accurate predictions. By leveraging these techniques, IDSs can adapt to evolving threats, detect previous attacks, and reduce false positives. This article proposes a novel IDS model based on DL algorithms like the Radial Basis Function Neural Network (RBFNN) and Random Forest (RF). The RF classifier is used for feature selection, and the RBFNN algorithm is used to detect intrusion in CC environments. Moreover, the datasets Bot-IoT and NSL-KDD have been utilized to validate our suggested approach. To evaluate the impact of our approach on an imbalanced dataset, we relied on Matthew’s Correlation Coefficient (MCC) as a normalized measure. Our method achieves accuracy (ACC) higher than 92% using the minimum features, and we managed to increase the MCC from 28% to 93%. The contributions of this study are twofold. Firstly, it presents a novel IDS model that leverages DL algorithms, demonstrating an improved ACC higher than 92% using minimal features and a substantial increase in MCC from 28% to 93%. Secondly, it addresses the security challenges specific to CC environments, offering a promising solution to enhance security in cloud systems. By integrating the proposed IDS model into cloud environments, cloud providers can benefit from enhanced security measures, effectively mitigating unauthorized access and potential data breaches. The utilization of DL algorithms, RBFNN, and RF has shown remarkable potential in detecting intrusions and strengthening the overall security posture of CC. Full article
(This article belongs to the Special Issue Security in Cloud Computing, Big Data and Internet of Things)
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27 pages, 665 KiB  
Article
An Evolutionary Game Theoretic Analysis of Cybersecurity Investment Strategies for Smart-Home Users against Cyberattacks
by N’guessan Yves-Roland Douha, Masahiro Sasabe, Yuzo Taenaka and Youki Kadobayashi
Appl. Sci. 2023, 13(7), 4645; https://doi.org/10.3390/app13074645 - 06 Apr 2023
Cited by 2 | Viewed by 1987
Abstract
In the digital era, smart-home users face growing threats from cyberattacks that threaten their privacy and security. Hence, it is essential for smart-home users to prioritize cybersecurity education and training to secure their homes. Despite this, the high cost of such training often [...] Read more.
In the digital era, smart-home users face growing threats from cyberattacks that threaten their privacy and security. Hence, it is essential for smart-home users to prioritize cybersecurity education and training to secure their homes. Despite this, the high cost of such training often presents a barrier to widespread adoption and accessibility. This study aims to analyze the costs and benefits associated with various cybersecurity investment strategies for smart-home users in the context of cyberattacks. The study utilizes evolutionary game theory to model a game comprised of three populations: smart-home users, stakeholders, and attackers. We derive and analyze the replicator dynamics of this game to determine the evolutionarily stable strategy (ESS). Furthermore, we investigate the impacts of the costs and benefits of cybersecurity investment and cyberattack costs on the ESS. The findings indicate that incurring costs for cybersecurity training is beneficial for smart-home users to protect their homes and families. However, the training costs must be low and affordable for smart-home users in order to ensure their participation and engagement. Additionally, providing rewards for commitment to cybersecurity is crucial in sustaining interest and investment over the long term. To promote cybersecurity awareness and training for smart-home users, governments can incorporate it as a priority in national cybersecurity plans, provide subsidies for training costs, and incentivize good cybersecurity practices. Full article
(This article belongs to the Special Issue Security in Cloud Computing, Big Data and Internet of Things)
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19 pages, 2870 KiB  
Article
Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
by Biao Xu and Minyan Lu
Appl. Sci. 2023, 13(6), 3703; https://doi.org/10.3390/app13063703 - 14 Mar 2023
Cited by 1 | Viewed by 1245
Abstract
The majority of cloud computing consists of servers with different configurations which host several virtual machines (VMs) with changing resource demands. Additionally, co-located VMs are vulnerable to co-resident attacks (CRA) in a networked environment. These two issues may cause uneven resource usage within [...] Read more.
The majority of cloud computing consists of servers with different configurations which host several virtual machines (VMs) with changing resource demands. Additionally, co-located VMs are vulnerable to co-resident attacks (CRA) in a networked environment. These two issues may cause uneven resource usage within the server and attacks on the service, leading to performance and security degradation. This paper presents an Agent-based VM migration solution that can balance the burden on commercially diverse servers and avoid potential co-resident attacks by utilizing VM live migrations. The Agent’s policies include the following: (i) a heuristic migration optimization policy to select the VMs to be migrated and the matching hosts; (ii) a migration trigger policy to determine whether the host needs to relocate the VMs; (iii) an acceptance policy to decide if the migration request should be accepted; and (iv) a balancer heuristic policy to make the initial VM allocation. The experiments and analyses demonstrate that the Agents can mitigate CRA in a distributed way to mitigate the associated risks while achieving acceptable load balancing performance. Full article
(This article belongs to the Special Issue Security in Cloud Computing, Big Data and Internet of Things)
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Review

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26 pages, 3517 KiB  
Review
A Survey of Secure Time Synchronization
by Ying Weng and Yiming Zhang
Appl. Sci. 2023, 13(6), 3923; https://doi.org/10.3390/app13063923 - 20 Mar 2023
Cited by 1 | Viewed by 2197
Abstract
Today, the use of wireless sensor networks has grown rapidly; however, wireless sensor networks are prone to receiving cyber-physical attacks. Time synchronization is a fundamental requirement for protocols in wired and wireless sensor network applications; hence, secure time synchronization is also crucial. This [...] Read more.
Today, the use of wireless sensor networks has grown rapidly; however, wireless sensor networks are prone to receiving cyber-physical attacks. Time synchronization is a fundamental requirement for protocols in wired and wireless sensor network applications; hence, secure time synchronization is also crucial. This paper presents an introduction to time synchronization, including the concepts, challenges, and requirements of time synchronization protocols. The scope of the paper includes both software- and hardware-based protocols. Then, different time synchronization methods are analyzed. Moreover, research progress in secure time synchronization is reviewed. The survey also discusses the weaknesses of current secure time synchronization protocols and provides suggestions for future research directions. This survey aims to highlight research progress and trends in time synchronization and secure time synchronization. Full article
(This article belongs to the Special Issue Security in Cloud Computing, Big Data and Internet of Things)
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