• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.1009-1019
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• 2018

Bitcoin is the first application utilizing the blockchain, but it has limitations in terms of scalability. The concept of Lightning Network was recently introduced to address the scalability problem of Bitcoin. In this paper, we found that the real-world Bitcoin Lightning Network shows the scale-free property. Therefore, the Bitcoin Lightning Network can be vulnerable to the intentional attacks targeting some specific nodes in the network while it is still robust to the random node failures. We experimentally analyze the robustness of the Bitcoin's Lightning Network via the simulation of network attack model. Our simulation results demonstrate that the real-world Lightning Network is vulnerable to target attacks that destroy a few nodes with high degree.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.103-110
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• 2022

Botnets have been exploited for a variety of purposes, ranging from monetary demands to national threats, and are one of the most threatening types of attacks in the field of cybersecurity. Botnets emerged as a centralized structure in the early days and then evolved to a P2P structure. Bitcoin is the first online cryptocurrency based on blockchain technology announced by Satoshi Nakamoto in 2008 and is the most widely used cryptocurrency in the world. As the number of Bitcoin users increases, the size of Bitcoin network is also expanding. As a result, a botnet using the Bitcoin network as a C&C channel has emerged, and related research has been recently reported. In this study, we propose an encrypted botnet C&C communication mechanism and technique in the Bitcoin network and validate the proposed method by conducting performance evaluation through various experiments after building it on the Bitcoin testnet. By this research, we want to inform the possibility of botnet threats in the Bitcoin network to researchers.

• KNOM Review
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• v.22 no.1
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• pp.68-76
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• 2019

Bitcoin, based on the blockchain technology is an online crypto-currency developed by Satoshi Nagamoto. Bitcoin, which was first issued on January 3, 2009, is rapidly evolving with increasing number of transactions. However, untoward incidents are occurring due to an increase in the number of Bitcoin transactions. Predicting the number of Bitcoin transactions is important to prepare for any issues that can occur in the Bitcoin network. This paper proposes to design model for predicting the number of Bitcoin transactions by applying two machine learning algorithms and then a model for predicting the number of Bitcoin transactions through experiments.

• KNOM Review
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• v.23 no.1
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• pp.26-33
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• 2020

Bitcoin is an electronic money that does not rely on centralized institutions such as banks and financial institutions, unlike the world's paper currencies such as dollar, won, euro and yen. In Bitcoin network, a block with transaction details is generated by mining, and the message that the block has been created is broadcast to all participating nodes in a broadcasting method to secure reliability through verification. Likewise, the mining and block propagation methods in the Bitcoin network are greatly affected by the performance of the P2P network. For example, in the case of mining, the node receiving the reward for mining varies depending on whether the block is first mined in the network and the proof of mining is propagated faster. In this paper, we applied local characteristics and Round-to-Trip(RTT) measurement to solve the problems of the existing neighbor connection method and block propagation method performed in Bitcoin network. An algorithm to improve block propagation speed is presented. Through experiments, we compare the performance of the improved algorithm with the existing algorithm to verify that the overall block propagation time is reduced.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.837-850
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• 2018

Bitcoin is a decentralized crypto-currency, which is based on the peer-to-peer network, and was introduced by Satoshi Nakamoto in 2008. Bitcoin transactions are written by using a scripting language. The hash value of a transaction's script is used to identify the transaction over the network. In February 2014, a Bitcoin exchange company, Mt. Gox, claimed that they had lost hundreds of millions US dollars worth of Bitcoins in an attack known as transaction malleability. Although known about since 2011, this was the first known attack that resulted in a company loosing multi-millions of US dollars in Bitcoins. Our reason for writing this paper is to understand Bitcoin transaction malleability and to propose an efficient solution. Our solution is a softfork (i.e., it can be gradually implemented). Towards the end of the paper we present a detailed analysis of our scheme with respect to various transaction malleability-based attack scenarios to show that our simple solution can prevent future incidents involving transaction malleability from occurring. We compare our scheme with existing approaches and present an analysis regarding the computational cost and storage requirements of our proposed solution, which shows the feasibility of our proposed scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1287-1295
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• 2021

During the past decade, it seems apparent that Bitcoin has been the best performing asset class. Even without a centralized authority that takes control over, Bitcoin, which started off with basically no value at all, reached around 65000 dollars in 2021, showing a movement that will definitely go down in history. Thus, even those who were skeptical of Bitcoin's intangible nature are stacking bitcoin as a huge part of their portfolios. Bitcoin's exponential growth in value also caught the attention of traditional banking and investment firms. Along with the spotlight Bitcoin is getting from the investment world, research using macro-economic variables and investor sentiment to explain Bitcoin's price movement has shown progress. However, previous studies do not make use of On-Chain Data, which are data processed using transaction data in Bitcoin's blockchain network. Therefore, in this paper, we will be utilizing LSTM, a method widely used for time-series data prediction, with On-Chain Data to predict the price of Bitcoin.

• KNOM Review
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• v.21 no.2
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• pp.18-25
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• 2018

Since the development of block-chain technology by Satoshi Nakamoto and Bitcoin pioneered a new cryptocurrency market, a number of scale of cryptocurrency have emerged. There are crimes taking place using the anonymity and vulnerabilities of block-chain technology, and many studies are underway to improve vulnerability and prevent crime. However, they are not enough to detect users who commit crimes. Therefore, it is very important to detect abnormal behavior such as money laundering and stealing cryptocurrency from the network. In this paper, the characteristics of the transactions and user graphs in the Bitcoin network are collected and statistical information is extracted from them and presented as plots on the log scale. Finally, we analyze visualized plots according to the Densification Power Law and Power Law Degree, as a result, present features appropriate for detection of anomalies involving abnormal transactions and abnormal users in the Bitcoin network.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.303-310
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• 2018

Bitcoin (BTC) is a cryptocurrency proposed by Satoshi Nakamoto in 2009. Bitcoin makes its transactions with no central authorities. This decentralization is accomplished with its mining, which is an operation that makes people compete to solve math puzzles to include new transactions into block, and eventually block chains (ledger) of bitcoin. Because miners need to solve a complex puzzles, they need a lot of computing resources. In return for miners' resources, bitcoin network gives newly minted bitcoins as a reward to miners when they succeed in mining. To prevent inflation, the reward is halved every 4 years. For example, in 2009 block reward was 50 BTC, but today, the block reward is 12.5 BTC. On the other hands, exchange rate for bitcoin and Korean Won (KRW) changed drastically from 924,000 KRW/BTC (January 12th, 2017) to 16,103,306 KRW/BTC (December 10th, 2017), which made mining more attractive. However, there are no rigorous researches on the profitability of bitcoin mining. In this paper, we evaluate the profitability of bitcoin mining.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.4
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• pp.81-92
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• 2018

Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.