• Health Policy and Management
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• v.32 no.1
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• pp.45-52
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• 2022

Background: There are regional variations in the incidence of coronavirus disease 2019 (COVID-19), which means that some regions are more exposed to the risk of COVID-19 than others. Therefore, this study aims to investigate regional variations in the incidence of COVID-19 in Korea and identify risk factors associated with the incidence of COVID-19 using community-level data. Methods: This study was conducted at the districts (si·gun·gu) level in Korea. Data of COVID-19 incidence by districts were collected from the official website of each province. Data was also obtained from the Korean Statistical Information Service and the Community Health Survey; socio-demographic factor, transmission pathway, healthcare resource, and factor in response to COVID-19. Community risk factors that drive the incidence of COVID-19 were selected using a least absolute shrinkage and selection operator regression. Results: As of June 2021, the incidence of COVID-19 differed by more than 80 times between districts. Among the candidate factors, sex ratio, population aged 20-29, local financial independence, population density, diabetes prevalence, and failure to comply with the quarantine rules were significantly associated with COVID-19 incidence. Conclusion: This study suggests setting COVID-19 quarantine policy and allocating resources, considering the community risk factors. Protecting vulnerable groups should be a high priority for these policies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2094-2114
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• 2022

Due to the wide application of fifth generation communication, wireless sensor networks have become an indispensable part in our daily life. In this paper, we analyze physical layer security for two-way relay with energy harvesting (EH), where power splitter is considered at relay. And two kinds of combined methods, i.e., selection combining (SC) and maximum ratio combining (MRC) schemes, are employed at eavesdropper. What's more, the closed-form expressions for security performance are derived. For comparison purposes, this security behaviors for orthogonal multiple access (OMA) networks are also investigated. To gain deeper insights, the end-to-end throughput and approximate derivations of secrecy outage probability (SOP) under the high signal-to-noise ratio (SNR) regime are studied. Practical Monte-Carlo simulative results verify the numerical analysis and indicate that: i) The secure performance of SC scheme is superior to MRC scheme because of being applied on eavesdropper; ii) The secure behaviors can be affected by various parameters like power allocation coefficients, transmission rate, etc; iii) In the low and medium SNR region, the security and channel capacity are higher for cooperative non-orthogonal multiple access (NOMA) systems in contrast with OMA systems; iv) The systematic throughput can be improved by changing the energy conversion efficiency and power splitting factor. The purpose of this study is to provide theoretical direction and design of secure communication.

• ETRI Journal
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• v.44 no.5
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• pp.733-745
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• 2022

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

• Parasites, Hosts and Diseases
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• v.60 no.4
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• pp.273-279
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• 2022

Laelapinae mites are involved in transmission of microbial diseases between wildlife and humans, with an impact on public health. In this study, 5 mite members in the subfamily Laelapinae (laelapin mites; LM) were morphologically identified by light microscopy, and the phylogenetic relationship of LM was analyzed in combination with the sequence information of part of the LM cytochrome oxidase subunit I (cox1) gene. The morphological identification revealed that 5 mites belonged to the genera Laelaps and Haemolaelaps, respectively. Sequence analysis showed that the ratio of nonsynonymous mutation rate to synonymous mutation rate of LM was less than 1, indicating that the LM cox1 gene had undergone purifying selection. Phylogenetic analysis showed that the Laelapinae is a monophyletic group. The genera Haemolaelaps and Hyperlaelaps did not separated into distinct clades but clustered together with species of the genus Laelaps. Our morphological and molecular analyses to describe the phylogenetic relationships among different genera and species of Laelapinae provide a reference for the improvement and revision of the LM taxonomy system.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.71-80
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• 2023

With the rapid advancement of the Internet, the use of encrypted traffic has surged in order to protect data during transmission. Simultaneously, network attacks have also begun to leverage encrypted traffic, leading to active research in the field of encrypted traffic analysis to overcome the limitations of traditional detection methods. In this paper, we provide an overview of the encrypted traffic analysis field, covering the analysis process, domains, models, evaluation methods, and research trends. Specifically, it focuses on the research trends in the field of anomaly detection in encrypted network traffic analysis. Furthermore, considerations for model development in encrypted traffic analysis are discussed, including traffic dataset composition, selection of traffic representation methods, creation of analysis models, and mitigation of AI model attacks. In the future, the volume of encrypted network traffic will continue to increase, particularly with a higher proportion of attack traffic utilizing encryption. Research on attack detection in such an environment must be consistently conducted to address these challenges.

• ETRI Journal
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• v.45 no.2
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• pp.254-266
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• 2023

This paper investigates the effect of hardware impairments (HIs) and imperfect channel state information (ICSI) on a SWIPT-assisted adaptive nonorthogonal multiple access (NOMA)/orthogonal multiple access (OMA) system over independent and nonidentical Rayleigh fading channels. In the NOMA mode, the energy-constrained near users act as a relay to improve the performance for the far users. The OMA transmission mode is adopted to avoid a complete outage when NOMA is infeasible. The best user selection scheme is considered to maximize the energy harvested and avoid error propagation. To characterize the performance of the proposed systems, closed-form and asymptotic expressions of the outage probability for both near and far users are studied. Moreover, exact and approximate expressions of the ergodic rate for near and far users are investigated. Simulation results are provided to verify our theoretical analysis and confirm the superiority of the proposed NOMA/OMA scheme in comparison with the conventional NOMA and OMA protocol with/without HIs and ICSI.

• Annual Conference of KIPS
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• 2011.04a
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• pp.158-159
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• 2011

Energy efficiency in wireless sensor networks (WSNs) is one significant factor that needs to be considered when making any designs or doing any enhancements on the communication protocol stack. In WSNs using traditional geographic routing, when a sensor node receives a data packet that needs to be transmitted to the sink, it will forward the packet to the neighbor node which is closest to the sink. The traditional geographic routing assumes that the link quality is always 100%. This may cause a bad result as per which we waste too many energy for retransmissions between the two nodes. Thus, the problem here is how to select such node as forwarder at most efficiently in the aspect of both energy consumption and the distance toward the destination. The better node we choose, the more energy we can conserve for the whole network. In this paper, we propose a next-hop forwarding selection metric, called Energy Consumption for Transmission (ECT), which can resolve the above problem in the best way.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.133-139
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• 2009

A P2P technology in distributed computing fields is presented various methods to share resources between network connected peers. This is very efficient that a degree of resources to good use as compared with peers by using centralized network by a few servers. However peers to compose P2P system is not always online status, therefore it is difficult to support high reliability to user. In our previous work of this paper, it is contributing to reduce the loading rates to select of new resource support peer but a selection method the peers to share works to download resources is very simple that it is just selected about peer to have lowest job. In this paper, we reduced frequency offline peers by estimate based on a average value of success rates for peers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1638-1658
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• 2024

As a crucial development direction for the sixth generation of mobile communication networks (6G), Low Earth Orbit (LEO) satellite networks exhibit characteristics such as low latency, seamless coverage, and high bandwidth. However, the frequent changes in the topology of LEO satellite networks complicate communication between satellites, and satellite power resources are limited. To fully utilize resources on satellites, it is essential to determine the association between satellites before power allocation. To effectively address the satellite association problem in LEO satellite networks, this paper proposes a satellite association-based resource allocation algorithm. The algorithm comprehensively considers the throughput of the satellite network and the fairness associated with satellite correlation. It formulates an objective function with logarithmic utility by taking the logarithm and summing the satellite channel capacities. This aims to maximize the sum of logarithmic utility while promoting the selection of fewer associated satellites for forwarding satellites, thereby enhancing the fairness of satellite association. The problems of satellite association and power allocation are solved under constraints on resources and transmission rates, maximizing the logarithmic utility function. The paper employs an improved Kuhn-Munkres (KM) algorithm to solve the satellite association problem and determine the correlation between satellites. Based on the satellite association results, the paper uses the Lagrangian dual method to solve the power allocation problem. Simulation results demonstrate that the proposed algorithm enhances the fairness of satellite association, optimizes resource utilization, and effectively improves the throughput of LEO satellite networks.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.115-124
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• 2024

In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contain a malicious and any illegal activity happened in network environments. To accomplish this we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifier are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.