• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.23-39
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• 2021

This study aims to explore research trends in Blockchain studies in South Korea using dynamic topic modeling and network analysis. To achieve this goal, we conducted the university & institute collaboration network analysis, the keyword co-occurrence network analysis, and times series topic analysis using dynamic topic modeling. Through the university & institute collaboration network analysis, we found major universities such as Soongsil University, Soonchunhyang University, Korea University, Korea Advanced Institute of Science and Technology (KAIST) and major institutes such as Ministry of National Defense, Korea Railroad Research Institute, Samil PricewaterhouseCoopers, Electronics and Telecommunications Research Institute that led collaborative research. Next, through the analysis of the keyword co-occurrence network, we found major research keywords including virtual assets (Cryptocurrency, Bitcoin, Ethereum, Virtual currency), blockchain technology (Distributed ledger, Distributed ledger technology), finance (Smart contract), and information security (Security, privacy, Personal information). Smart contracts showed the highest scores in all network centrality measures showing its importance in the field. Finally, through the time series topic analysis, we identified five major topics including blockchain technology, blockchain ecosystem, blockchain application 1 (trade, online voting, real estate), blockchain application 2 (food, tourism, distribution, media), and blockchain application 3 (economy, finance). Changes of topics were also investigated by exploring proportions of representative keywords for each topic. The study is the first of its kind to attempt to conduct university & institute collaboration networks analysis and dynamic topic modeling-based times series topic analysis for exploring research trends in Blockchain studies in South Korea. Our results can be used by government agencies, universities, and research institutes to develop effective strategies of promoting university & institutes collaboration and interdisciplinary research in the field.

• The Journal of Bigdata
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• v.8 no.2
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• pp.125-131
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• 2023

Globally, drug side effects rank among the top causes of death. To effectively respond to these adverse drug reactions, a shift towards an active real-time monitoring system, along with the standardization and quality improvement of data, is necessary. Integrating individual institutional data and utilizing large-scale data to enhance the accuracy of drug side effect predictions is critical. However, data sharing between institutions poses privacy concerns and involves varying data standards. To address this issue, our research adopts a federated learning approach, where data is not shared directly in compliance with privacy regulations, but rather the results of the model's learning are shared. We employ the Common Data Model (CDM) to standardize different data formats, ensuring accuracy and consistency of data. Additionally, we propose a drug monitoring system that enhances security and scalability management through a cloud-based federated learning environment. This system allows for effective monitoring and prediction of drug side effects while protecting the privacy of data shared between hospitals. The goal is to reduce mortality due to drug side effects and cut medical costs, exploring various technical approaches and methodologies to achieve this.

• Korean Security Journal
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• no.61
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• pp.59-85
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• 2019

In the reality that the boundary between the real world and the virtual world disappears with the 4th Industrial Revolution, cyber crimes that occur beyond time and space have clear limitations in fulfilling their duties only with the police force of government organizations established under the real law system. The research method of this thesis is based on the literature research and the experience of security work. The purpose of this paper is to establish a social system where collective intelligence of each social field can participate voluntarily to respond to cyber crimes occurring beyond the time and space before the law and institutionalization. In addition, the social system in which collective intelligence in each social sector can participate voluntarily was established to define crime types in cyberspace in real time and to prevent crimes defined by the people themselves and the counter-measures had been proposed in order to form social consensus. First, it is necessary to establish a collective intelligent network-type cyberpolice volunteer system. The organization consists of professors of security and security related departments at universities nationwide, retired public officials from the National Intelligence Service, the National Police Agency, and the National Emergency Management Agency, security companies and the organizations, civilian investigators, security & guard, firefighting, police, transportation, intelligence, security, national security, and research experts. Second, private sector regulation should be established newly under the Security Business Act. Third, the safety guard of the collective intelligent cyberpolice volunteer system for the stability of the people's lives should strengthen volunteer work. Fourth, research lessons and legal countermeasures against cybercrime in advanced countries should be introduced. Fifth, the Act on the Protection of Personal Information, the Act on Promotion of Information and Communication Network Utilization and Information Protection, the Act on the Utilization and Protection of Credit Information, and the Special Act on the Materials and Parts Industry should be amended. Sixth, police officers should develop cybercrime awareness skills for proactive prevention activities.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.143-157
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• 2008

Ubiquitous Web Services is able to be specified future Web Services technology for connecting with various application services in any device and network environments. The devices, in ubiquitous environment, have dynamic characteristic such as location and statuse. So, we must support methods of dynamic service discovery in ad-hoc network. There are many related works at transaction, security, QoS, semantic and Web Services composition with various fields. Recently, the studies are interested in the Ubiquitous by development of computing and network technology. However, they are an early stage. For this reason, in this paper, we propose a WSUN(Web Services on Universal Networks) for Ubiquitous Web Services. It is a SOA based framework. And this paper extracts necessity of WSUN environment from scenario. The framework is composed of US Broker(Universal Service Broker). It is designed for satisfying the conditions and supports dynamic service discovery using a US Registry (Universal Service Registry). Consequently. clients are able to discover and use Universal Service by protocol stack of the US Broker for Web Services. And it is a strong point which supports interoperability between heterogeneous networks.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.395-403
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• 2024

Recently, with the advancement of technology, the automotive industry has seen an increase in network connectivity. CAN (Controller Area Network) bus technology enables fast and efficient data communication between various electronic devices and systems within a vehicle, providing a platform that integrates and manages a wide range of functions, from core systems to auxiliary features. However, this increased connectivity raises concerns about network security, as external attackers could potentially gain access to the automotive network, taking control of the vehicle or stealing personal information. This paper analyzed abnormal messages occurring in CAN and confirmed that message occurrence periodicity, frequency, and data changes are important factors in the detection of abnormal messages. Through DBC decoding, the specific meanings of CAN messages were interpreted. Based on this, a model for classifying abnormalities was proposed using the GRU model to analyze the periodicity and trend of message occurrences by measuring the difference (residual) between the predicted and actual messages occurring within a certain period as an abnormality metric. Additionally, for multi-class classification of attack techniques on abnormal messages, a Random Forest model was introduced as a multi-classifier using message occurrence frequency, periodicity, and residuals, achieving improved performance. This model achieved a high accuracy of over 99% in detecting abnormal messages and demonstrated superior performance compared to other existing models.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• The Korean Society of Law and Medicine
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• v.23 no.4
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• pp.29-74
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• 2022

As social disasters occur under the Disaster Management Act, which can damage the people's "life, body, and property" due to the rapid spread and spread of unexpected COVID-19 infectious diseases in 2020, information collected through inspection and reporting of infectious disease pathogens (Article 11), epidemiological investigation (Article 18), epidemiological investigation for vaccination (Article 29), artificial technology, and prevention policy Decision), (3) It was used as an important basis for decision-making in the context of an infectious disease crisis, such as promoting vaccination and understanding the current status of damage. In addition, medical policy decisions using infectious disease data contribute to quarantine policy decisions, information provision, drug development, and research technology development, and interest in the legal scope and limitations of using infectious disease data has increased worldwide. The use of infectious disease data can be classified for the purpose of spreading and blocking infectious diseases, prevention, management, and treatment of infectious diseases, and the use of information will be more widely made in the context of an infectious disease crisis. In particular, as the serious stage of the Disaster Management Act continues, the processing of personal identification information and sensitive information becomes an important issue. Information on "medical records, vaccination drugs, vaccination, underlying diseases, health rankings, long-term care recognition grades, pregnancy, etc." needs to be interpreted. In the case of "prevention, management, and treatment of infectious diseases", it is difficult to clearly define the concept of medical practicesThe types of actions are judged based on "legislative purposes, academic principles, expertise, and social norms," but the balance of legal interests should be based on the need for data use in quarantine policies and urgent judgment in public health crises. Specifically, the speed and degree of transmission of infectious diseases in a crisis, whether the purpose can be achieved without processing sensitive information, whether it unfairly violates the interests of third parties or information subjects, and the effectiveness of introducing quarantine policies through processing sensitive information can be used as major evaluation factors. On the other hand, the collection, provision, and use of infectious disease data for research purposes will be used through pseudonym processing under the Personal Information Protection Act, consent under the Bioethics Act and deliberation by the Institutional Bioethics Committee, and data provision deliberation committee. Therefore, the use of research purposes is recognized as long as procedural validity is secured as it is reviewed by the pseudonym processing and data review committee, the consent of the information subject, and the institutional bioethics review committee. However, the burden on research managers should be reduced by clarifying the pseudonymization or anonymization procedures, the introduction or consent procedures of the comprehensive consent system and the opt-out system should be clearly prepared, and the procedure for re-identifying or securing security that may arise from technological development should be clearly defined.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.12a
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• pp.543-559
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• 2003

The information drive of the businesses requires new alternatives in that the promotion of business efficiency through information process technologies ends up conflicting with the protection of information human rights on laborers’side. Nevertheless, apathy on information protection has a tendency to be distorted by the efficiency of the businesses. Should the capital and mass media warn economic red lights, political circles with uneasiness would ignore the significance of information protection on the behalf of business efficiency. Therefore, the importance of information protection is considered a smaller interest than that of business efficiency with the infringements of human rights on laborers’side arising. Informatization of the businesses along with the developments of information process technologies has enabled the management to monitor and control the behaviors of laborers. This new problem needs to establish both information protection mechanism and institutional devices to regulate those labor controls. The security of business activity without human rights infringement warrants both basic rights of the public and spirit of the Constitution. The study suggests the establishment and revision of laws suitable to the period of information human rights. On top of that, the establishment of the basic law for information protection of individuals’with the common principle that integrates the related laws and rules on-off line is needed. This will warrant the active participation of labor unions and create specific alternatives for information protection.

• Journal of Global Scholars of Marketing Science
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• v.17 no.1
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• pp.101-122
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• 2007

Recently, as internet shopping mall users rapidly, a form of shopping changed from off line to on line. The rapid growth of customers and transaction volume through evolution of new media, internet, brings new problems to internet marketers. It is the most important task that how internet shopping mall operators obtain their customers trust and repeat buying. This empirical research investigates online shoppers for their trust dimensions for online retailers. The study aimed to determine whether e-trust antecedents(perceived reputation, perceived quality, perceived value) influence trust dimension and whether the multidimensional trust contributed differently to perceived risk and willingness to depend on e-retailers. Consequences of the research are as follows: First, it reveals that of reputation, web site quality of the internet shopping mall have influence upon trust dimension. Second, the higher level of trust consumers have, the higher level of willingness to depend and intent to revisit on the retailers they have. But level of perceive risk consumers have not influences on willingness to depend on the retailers. It is necessary for internet shopping mall to development its reputation and familiarity to obtain customer's trust. Accordingly, this research will be helping internet shopping mall insight for marketing strategies, constantly should study about action and mind of consumer.