Bitcoin proved the potential of blockchain technology through decentralized, transparent, and immutable transactions. However, there are still challenges for fast and stable transitions. Optimizing block propagation times within the network is one of them. Prolonged propagation times can restrict efficiency, scalability, and security. This paper presents a novel approach to reducing block propagation time through leveraging reinforce-ment learning (RL) for the node’s neighbor selection strategies. We implemented a Deep Q-Network (DQN) model in minimizing block receive times at each node, thereby impacting overall block propagation time. We used a model that defines node states based on latencies of outbound connections, which present the node’s region. By evaluating this model through simulations using SimBlock, a robust Bitcoin network simulator, we observed a significant reduction in block propagation time—approximately 30% for smaller networks and 20% for larger ones. Our analysis extended to node connections generated by our model and comparative evaluation against existing methodologies.

Unlike the traditional stock markets, the 24/7 nature of the cryptocurrency market poses unique challenges and opportunities, particularly in asset trading and management. These dynamic market conditions have accelerated the development of sophisticated trading strategies, increasingly leveraging the power of Artificial Intelligence (AI). Among these, AI-driven trading bots have become a prominent tool, offering enhanced decision-making capabilities over conventional methods. This paper proposes the application of the Advantage Actor-Critic (A2C) model, a reinforcement learning technique ideally suited for the unpredictable nature of the cryptocurrency market. Our research aims to optimize asset allocation within a diverse portfolio, including both high-volatility cryptocurrencies and the more stable US Dollar. The proposed A2C model strategically leverages current and predicted price data of cryptocurrencies with current asset allocation to make new asset allocation decisions. Our experiments demonstrate the A2C model’s efficacy in managing asset allocations under varying market conditions. We particularly focus on how the model responds to alterations in the loss penalty factor within its reward function, which enables a shift between aggressive and conservative investment strategies. The model effectively balances risk and return, showing promising potential in achieving stable asset growth in rising markets while mitigating losses during market downturns.

With the rise of NFTs, which serve as proof of ownership for assets, security tokens that enable transactions without intermediaries have become a popular topic. Tokenization eliminates the need for centralized markets and allows for fast trades. In addition to tokenization, there are attempts to increase asset liquidity by fractionalizing them, a concept known as fractional ownership. Despite the existence of some platforms that use tokenization and fractional ownership, there is still limited research on fractional NFTs, and institutional support is lacking. In this paper, we propose possible implementations of fractional NFTs and evaluate their gas costs, which are crucial for providing fractional NFT-related services. As most NFTs are minted based on the Ethereum blockchain, we implement fractional NFTs using ERC standards. Our evaluation shows that ERC-721 or ERC-1155 NFTs fractionalized into ERC-20 FTs have the lowest long-term gas costs.

Bitcoin transactions include unspent transaction outputs (UTXOs) as their inputs and generate one or more newly owned UTXOs at specified addresses. Each U TXO can only be used as an input in a transaction once, and using it in two or more different transactions is referred to as a double-spending attack. Ultimately, due to the characteristics of the Bitcoin protocol, double-spending is impossible. However, problems may arise when a transaction is considered final even though i ts finality has not been fully guaranteed in order to achieve fast payment. In this paper, we propose an approach to detecting Bitcoin double-spending attacks using a graph neural network (GNN). This model predicts whether all nodes in the network contain a given payment transaction in their own memory pool (mempool) using information only obtained from some observer nodes in the network. Our experiment shows that the proposed model can detect double-spending with an accuracy of at least 0.95 when more than about 1% of the entire nodes in the network are observer nodes.

Managing identity is a crucial issue, and with the advent of digital identity, Self-Sovereign Identity (SSI) has been highlighted. SSI allows the owner to have sovereignty over their identity information. Currently, blockchain technology is widely used to implement SSI management system. Issuing a verifiable credential (VC) based on blockchain has two advantages: it ensures the VC has not been tampered with and makes the VC trustworthy by sharing the same ledger among untrusted parties. This paper presents a SSI management system which was designed and implemented for police or firefighters who still manage identity information centrally. Furthermore, given the inherent risks associated with these occupations, it is imperative to have a secure healthcare system to meet their specific needs. We propose a system that links these healthcare systems, allowing owners of identity information to monitor how their identity information has been used. The usage history of the system is stored in a permissioned blockchain, specifically Hyperledger Fabric.

Non-Fungible Tokens (NFT) is a unique digital token based on blockchain technology, which is used as a means to prove ownership of assets in various areas. With the huge popularity of NFT, the launch of new NFTs attracted many people to mint NFTs. However, because Ethereum has low processing speed and then cannot accommodate the explosive demands, it causes Crypto Gas War, which increases the overall transaction fee and wastes unnecessary gas. In this work, we propose a Raffle-based N FT launch to solve t he C rypto Gas War. We demonstrated that our proposed NFT launch solves this problem but other existing solutions do not solve the problem. Moreover, our NFT smart contract is gas-efficient. Our proposed method reduces the gas usage by 15.5% or more compared to general method, and the efficiency is improved a ccording to increasing the total number of NFTs.

As blockchain protocols exhibit diverse characteristics and performances, it is crucial to decide whether to develop a custom blockchain protocol, select an existing platform, or identify a promising protocol before initiating a blockchain-based service or starting a new business. To assist the general public and business operators in assessing the desirability of each blockchain protocol, various services provide evaluation and ranking services for blockchain projects. However, most of these services rely on qualitative evaluation based on reports provided by the blockchain development team. Therefore, we propose a quantitative evaluation method for blockchain protocols that compares and analyzes the technological status, future development direction, and technological differences of Layer 1 and Layer 2 protocols. Our approach incorporates indicators that are distinct from other services and indicators specific to layer 2 solutions, facilitating a more comprehensive analysis of blockchain protocols. This allows for objective evaluation of protocol performance and mutual comparison between protocols.

Several years after NFTs first appeared, people began to use NFT to prove ownership of assets in diverse domains. Then the values of NFTs rapidly increased and accordingly the interests in NFTs have grown. Because Ethereum has the most active transactions and has a lot of users, many projects use Ethereum to deploy their NFT smart contracts. However, Ethereum has a chronic disadvantage of low scalability. During the NFT drop period for famous and popular NFTs, users are crowding the event, resulting in a large number of transactions to get NFTs. The low scalability leads to a fierce competition called the Crypto Gas War. In this work, we choose three famous NFT drop events to collect on-chain data of Ethereum over the drop period in order to analyze the impact of the Crypto Gas War on the Ethereum network in detail. In addition, we analyze the collected data to uncover critical and hidden problems, and present insights to solve them.

In accordance with the guidelines of the Financial Action Task Force (FATF), Virtual Asset Service Providers (VASPs) should comply with a ‘travel rule’, which requires them to exchange originator’s and beneficiary’s personal information when transferring virtual assets. In this paper, we propose a novel blockchain-based travel rule compliance system that supports fully-decentralized data exchange. The proposed system uses a permissioned blockchain, and thereby eliminates the possibility of leakage of personal information to third parties or even to travel rule service providers, and ensures that travel rule data can be managed securely.

With the advent of e-commerce and electronic payment systems, the use of paper currency is decreasing. Therefore, it is sufficiently predictable that most paper currencies will disappear and digital currencies will become the mainstream. This phenomenon is further accelerated by advances in blockchain technology and COVID-19. This is why the Central Bank Digital Currency (CBDC) has recently begun to attract attention. Currently, there are studies on CBDC with a blockchain-based distributed ledger. In this paper, we propose Cosmos blockchain based CBDC (Cos-CBDC) that enables communication between blockchains using Inter-Blockchain Communication (IBC) protocol to ensure interoperability. We not only analyze the requirements of Cos-CBDC but also design and implement it using Cosmos-SDK. Furthermore, we propose a Group Key Management system in Cos-CBDC. It can give different user privileges, and privacy-preserving is possible in the key generation process.

Bitcoin users are guaranteed to be anonymous, increasing the number of cryptocurrency trading related to crimes and fraudulent activities. While most studies about detecting illegal transactions try to distinguish trading patterns and classify them from legitimate ones, classification performance is poor since the class distributions of transaction data are highly imbalanced. In general, the Synthetic Minority Over-sampling TEchnique (SMOTE) is used to deal with class-imbalanced data, but SMOTE has a problem that it does not fully represent the diversity of the data. In this paper, we introduce another oversampling technique using Generative Adversarial Networks (GAN) to generate artificial training data for classification model. In order to verify similarity between artificial data and the actual one, oversampled dataset is evaluated with a classification model using XGBoost algorithm. We show classification performance is improved on average with synthetic data generated by both SMOTE and well-designed GAN model.

Supply chain is an entire network of producing and delivering a specific product to a final consumer. Stainless steel is a specific product being delivered on a supply chain. It is not easy to manage and monitor the entire supply chain because a supply chain has high complexity, including various organizations and activities. Due to this difficulty, several issues occur in the process of supplying stainless steel, such as forgery and alteration. Blockchain is a decentralized and distributed ledger technology that specializes in transparency and immutability. Many companies try to introduce this blockchain technology into supply chain management to conveniently monitor their supply chain. Accordingly, the blockchain technology can make steel companies be able to investigate and protect high-quality products from counterfeited low quality products. This paper proposes a design of a blockchain-based stainless steel tracking system to thoroughly track the entire process involved in supplies from stainless steel mills to the final customers. This proposed design is based on the hyperledger fabric which is one of the most popular private blockchain platforms.