• Electronics and Telecommunications Trends
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• v.37 no.1
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• pp.63-72
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• 2022

Recently, blockchain has been attracting attention as a high-reliability technology in various fields. However, the Proof-of-Work-based distributed consensus algorithm applied to representative blockchains, such as Bitcoin and Ethereum, has limitations in applications to various industries owing to its excessive resource consumption and performance limitations. To overcome these limitations, various distributed consensus algorithms have appeared, and recently, hybrid distributed consensus algorithms that use two or more consensus algorithms to achieve decentralization and scalability have emerged. This paper introduces the technological trends of the latest high-performance distributed consensus algorithms by analyzing representative hybrid distributed consensus algorithms.

• Knowledge Management Research
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• v.23 no.1
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• pp.27-46
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• 2022

The current era's focus is on the surge in real estate prices triggered by the global economic downturn. This study advocated STO-based dispersed investment for the MZ generation, who has less capital than earlier generations. Existing real estate investment methods were categorized into online, offline, and hybrid formats and the effectiveness of the suggested STO was given in this study through case analysis domestically and overseas. The entry of STO into the financial industry was positively proved, and the efficacy of blockchain technology was validated, through the investigation of the STO framework. The findings of this study are projected to revitalize the new real estate sector by actively supporting the access of the MZ generation into the current inflexible real estate investment market by the application of blockchain and reflecting MZ generation's investment propensity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.51-56
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• 2020

Cryptocurrencies, including Bitcoin, has decentralization, distribution and P2P properties unlike traditional currencies relies on trusted central party such as banks. All transactions are stored transparently and distributively, hence all participants can check the details of those transactions. Due to the properties of cryptographic hash function, deletion or modification of the stored transations is computationally not possible. However, cryptocurrencies only provide pseudonymity, not anonymity, which is provided by traditional currencies. Therefore many researches were conducted to provide anonymity to cryptocurrencies such as mix-based methods. In this paper, I will propose more efficient hybrid mix-based method for anonymity than previous mix-based one.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.2
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• pp.155-168
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• 2023

Health geography has gained importance due to healthy smart cities, regions, and the integration of geo-internet and blockchain technologies. This study explores the intersection of geography and health, focusing on specific health challenges faced by individuals and groups. Using observational and descriptive methods, the study takes a regional approach to illuminate the socio-economic factors that are critical to addressing global health challenges. Drawing on academic literature and practical research, a concise case study of health challenges in Uzbekistan is presented, offering valuable insights. The analysis of data from informative articles and UN publications highlights the interdisciplinary nature of health geography and its practical applicability for researchers and policymakers. The findings underscore the important role of geography and health sciences in addressing region-specific diseases while highlighting the importance of spatial analysis in understanding environmental hazards and health impacts, including disease outbreaks.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.60-66
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• 2024

Host's data during transmission. Data tempering results in loss of host's sensitive information, which includes number of VM, storage availability, and other information. In the distributed cloud environment, each server (computing server (CS)) configured with Local Resource Monitors (LRMs) which runs independently and performs Virtual Machine (VM) migrations to nearby servers. Approaches like predictive VM migration [21] [22] by each server considering nearby server's CPU usage, roatative decision making capacity [21] among the servers in distributed cloud environment has been proposed. This approaches usage underlying server's computing power for predicting own server's future resource utilization and nearby server's resource usage computation. It results in running VM and its running application to remain in waiting state for computing power. In order to reduce this, a decentralized decision making hybrid model for VM migration need to be proposed where servers in decentralized cloud receives, future resource usage by analytical computing system and takes decision for migrating VM to its neighbor servers. Host's in the decentralized cloud shares, their detail with peer servers after fixed interval, this results in chance to tempering messages that would be exchanged in between HC and CH. At the same time, it reduces chance of over utilization of peer servers, caused due to compromised host. This paper discusses, an roatative decisive (RD) approach for VM migration among peer computing servers (CS) in decentralized cloud environment, preserving confidentiality and integrity of the host's data. Experimental result shows that, the proposed predictive VM migration approach reduces extra VM migration caused due over utilization of identified servers and reduces number of active servers in greater extent, and ensures confidentiality and integrity of peer host's data.