• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.123-126
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• 2017

비트코인은 각국의 중앙은행이 화폐 발행을 독점하고 자의적인 통화 정책을 펴는 것에 대한 반발로 만들어진 암호화 된 디지털 화폐이며, 2008년 10월에 처음 등장하였다. 새로운 전자화폐로 성장한 비트코인은 현재 여러 나라에서 서비스되고 있으나 보안상의 취약점들이 발견되면서 이를 보완해야 할 필요성이 대두되고 있다. 본 논문에서는 비트코인의 특징과 서비스현황을 알아보고 비트코인의 취약점 중 지나친 익명성, 이기적인 채굴 그리고 51% Attack의 취약점들을 대상으로 분석하고, 대응 방안에 대한 한계를 제시한다.

• The Journal of Information Systems
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• v.29 no.1
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• pp.289-301
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• 2020

Purpose This study analyzes whether Internet search of cryptocurrency has a causal relationship to return and volatility of cryptocurrency. Design/methodology/approach Google Trend was used as a measure of the level of Internet search, and the parametric tests of Granger causality in the 1st moment and the 2nd moment were adopted as the analysis method. We used Bitcoin's dollar-based price, which is the No. 1 market value among cryptocurrency. Findings The results showed that the Internet search measured by Google Trends has a causal relationship to cryptocurrency in both average and volatility, while there is a difference in causality and its degree according to the search area and category that Google Trend user should set. Because the Granger causality is based on the improvement of prediction, the analysis results of this study indicate that Internet search can be used as a leading indicator in predicting return and volatility of cryptocurrency.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.191-200
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• 2022

These days, businesses, in both online and offline, have started accepting cryptocurrencies as payment methods. Even in countries like El Salvador, cryptocurrencies are recognized as fiat currencies. Meanwhile, publicly known, but not patched software vulnerabilities are security threats to not only software users but also to our society in general. As the status of cryptocurrencies has gradually increased, the impact of security vulnerabilities related to cryptocurrencies on our society has increased as well. In this paper, we first analyze vulnerabilities from the two major cryptocurrency vendors of Bitcoin and Ethereum in a quantitative manner with the respect to the CVSS, to see how the vulnerabilities are roughly structured in those systems. Then we introduce a modified AML vulnerability discovery model for the vulnerability datasets from the two vendors, after showing the original AML dose not accurately represent the vulnerability discovery trends on the datasets. The analysis shows that the modified model performs better than the original AML model for the vulnerability datasets from the major cryptocurrencies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1398-1404
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• 2018

The Blockchain technology could represent a solution to unexpected problems. In modern society, the domain of value has become diversified. The Crypto currency would replace the area that conventional currency could not function. The Japanese government recognized the Bitcoin as an official currency from April 2017 through amendment of the Money Transfer Act in 2016, and has been leading the related regulation policy recently. This paper examines the policy strategy of the Japanese government that is leading the current policy related to the Crypto currency market. The sequence of this paper is as follows: Coordination Process of Japanese Government's Crypto currency Policy of Special Mission Committee on IT Strategy as a Coordinator, Amendment of the Fund Settlement Act and the Establishment of a Self-Regulation Organizations in Crypto currency and Japan's Crypto currency Taxation and Supplementary System.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.59-67
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• 2021

Blockchain technology began in 2008 when an unidentified person named Satoshi Nakamoto proposed a cryptocurrency called Bitcoin. Satoshi Nakamoto had distrust of the existing financial system and wanted to implement a financial system that is robust against hacking or mannipulation without a middleman such as a bank through blockchain technology. Satoshi proposed a blockchain as a technology to prevent the creation of the bitcoin and forging of transactions, and through this, the functions of issuance, transaction, and verification of currency were implemented. Since then, Ethereum, a cryptocurrency that can implement the smart contract on the blockchain, has been developed, allowing financial products that require complex contracts such as deposits, loans, insurance, and derivatives to be brought into the area of cryptocurrency. In addition, it is expanding the possibility of substituting products provided by financial institutions through combination with real assets. These applications are defined as Decentralized Finance (DeFi). This paper was prepared to understand the overall technical understanding of DeFi and to introduce the services currently in operation. First, the technologies and ecosystems that implement the overall DeFi are explained, and then the representative DeFi services are categorized by feature and described.

• KNOM Review
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• v.22 no.3
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• pp.20-24
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• 2019

Blockchain is a distributed ledger-based technology where all nodes participating in the blockchain network are connected to the P2P network. When a transaction is created in the blockchain network, the transaction is propagated and validated by the blockchain nodes. The verified transaction is sent to peers connected to each node through P2P network, and the peers keep the transaction in the memory pool. Due to the nature of P2P networks, the number and type of transactions delivered by a blockchain node is different for each node. As a result, all nodes do not have the same memory pool. Research is needed to solve problems such as attack detection. In this paper, we analyze transactions in the memory pool before solving problems such as transaction fee manipulation, double payment problem, and DDos attack detection. Therefore, this study collects transactions stored in each node memory pool of Bitcoin and Ethereum, a cryptocurrency system based on blockchain technology, and analyzes how much common transactions they have using jacquard similarity.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.105-117
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• 2019

Bitcoin, which can be classified as a cryptocurrency, has attracted attention from various industries because it is an innovative digital currency and the beginning of a Blockchain system. However, as the research on Bitcoin progressed, several security vulnerabilities and possible attacks were analyzed. Among them, the security problem caused by the transparency of the Blockchain database prevents the Blockchain system from being applied to various fields. This vulnerability is further classified as the weak anonymity of participating nodes and privacy problem due to disclosure of transaction details. In recent years, several countermeasures have been developed against these vulnerabilities. In this paper, we first describe the main features of the public and private Blockchain, and explain privacy, unlinkability and anonymity. And, three public Blockchain platforms, Dash, Zcash and Monero which are derived from Bitcoin, and Hyperledger Fabric which is a private Blockchain platform, are examined. And we analyze the operating principles of the protocols applied on each platform. In addition, we classify the applied technologies into anonymity and privacy protection in detail, analyze the advantages and disadvantages, and compare the features and relative performance of the platforms based on the computational speed of the applied cryptographic mechanisms.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.17-26
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• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.384-384
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• 2022

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.47-63
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• 2019

The volatility of major crypto currencies was examined and they are diagnosed whether they have a systematic risk or not, by estimating market beta representing systematic risk using GARCH( Generalized Auto Regressive Conditional Heteroskedastieity) model. First, the empirical results showed that their prices are very volatile over time because of the existence of ARCH and GARCH effects. Second, in terms of efficiency, asymmetric GJR model was estimated to be the most appropriate model because the standard error of a market beta was less than that of the OLS model and GARCH model. Third, the estimated market beta of Bitcoin using GJR model was less than 1 at 0.8791, showing that there is no systematic risk. However, unlike OLS model, the market beta of Ethereum and Ripple was estimated at 1.0581 and 1.1222, showing that there is systematic risk. This result shows that bitcoin is less dangerous than Ripple and Ethereum, and ripple is the most dangerous of all three crypto currencies. Finally, the major cryptocurrency found that the negative impact caused greater variability than the positive impact, causing bad news to fluctuate more than good news, and therefore good news and bad news had a different effect on the variability.