Future Security of NFT-Blockchain

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

Deadline for manuscript submissions: 20 September 2024 | Viewed by 775

Special Issue Editor


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Guest Editor
Division of Computer Science and Engineering, Sun Moon University, Asan-si 31460, Chung Nam, Republic of Korea
Interests: cyber-security; context-aware security; multicast security (MSEC); IoT security; AI security; AI ATP; anomaly detection; GNN (Graph Neural Networks); generative AI security; digital twin
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Special Issue Information

Dear Colleagues,

A core function of cyber-security is to protect connected devices, such as IoT devices, and the services we access, both online and at work. In threat analyses of cyber-security, in which researchers summarize the various sources of threats, the results could appear biased without the use of objective methodology. To solve this bias problem, we use meta-analysis. Once the statistical results are combined with the meta-analysis, their statistical power and precision are increased because the sample size is significantly larger compared to those of individual studies. Meta-analysis is used in various fields, such as economics, sociology, psychology, education, and medicine, to statistically integrate and analyze the quantitative effect size results of different studies. Recently, analyses of the technology transfer performance of public research institutes and technology commercialization promotion, performance enhancement analysis, and educational research methods have been used.

With modern society becoming more complex and diversified, more social issues will arise in the digital age. Therefore, this Special Issue aims to publish research papers on meta-analyses for future applications. We welcome contributions on, but not limited to, the following topics:

  • Meta-analysis of learning for cyber-security
  • Meta-analysis of digital side-effects in cyber-security
  • Meta-analysis of semantic networks in cyber-security
  • Generative AI in security monitoring
  • Analysis of future security in generative AI
  • Cyber-security for NFTs
  • AR/VR/XR/Metaverse security
  • NFT-Blockchain applications
  • Secure communication in blockchains
  • Fake news and deep fake detection

Dr. Hoon Ko
Guest Editor

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Keywords

  • meta-analysis of learning for cyber-security
  • meta-analysis of digital side-effects in cyber-security
  • meta-analysis of semantic networks in cyber-security
  • generative AI in security monitoring
  • analysis of future security in generative AI
  • cyber-security for NFTs
  • AR/VR/XR/Metaverse security
  • NFT-blockchain applications
  • secure communication in blockchains
  • fake news and deep fake detection

Published Papers (1 paper)

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Research

18 pages, 2786 KiB  
Article
Digital Content Management Using Non-Fungible Tokens and the Interplanetary File System
by Hoon Ko, Juhee Oh and Sung Uk Kim
Appl. Sci. 2024, 14(1), 315; https://doi.org/10.3390/app14010315 - 29 Dec 2023
Viewed by 501
Abstract
Non-fungible tokens (NFTs) are digital identifiers containing metadata, such as token number, title, content, and image URL, and are linked to digital assets, which are characterized by the fact that, unlike conventional virtual assets, they have their own unique value and cannot be [...] Read more.
Non-fungible tokens (NFTs) are digital identifiers containing metadata, such as token number, title, content, and image URL, and are linked to digital assets, which are characterized by the fact that, unlike conventional virtual assets, they have their own unique value and cannot be replaced. NFTs cannot be deleted or forged; therefore, they can be used to authenticate the ownership of digital assets. The metadata of the NFTs are uploaded to the interplanetary file system (IPFS), which is a distributed file system, and converted into unique content identifiers (CIDs) that are stored on the blockchain. Digital content (DC) is divided into multiple pieces; it also has its own unique value and is distributed and stored using the IPFS. This study built an NFT-based IPFS testbed and experimented with the process of generating unique values for DC divided into three groups and sharing them. The results confirmed that each DC had a unique hash value and no duplicates existed. Full article
(This article belongs to the Special Issue Future Security of NFT-Blockchain)
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