Blockchain Papers

Emmanuel Asiamah, Nana Kwadwo Akrasi‐Mensah, Prince Odame, Eliel Keelson · 8 authors

No abstract is available for this record.

Igba Emmanuel, Moral Kuve Ihimoyan, Babatunde Awotiwon, Akinkunmi Rasheed Apampa

This paper explores the integration of advanced machine learning models, including BERT, GPT, and the Prophet algorithm, with finance investment strategies to enhance predictive modeling and trend analysis in blockchain technology. The rapid evolution of blockchain has transformed financial ecosystems, offering decentralized platforms for secure and transparent transactions. However, predicting market trends and investment opportunities within this domain remains a complex challenge due to high volatility and the multifaceted nature of financial data. By leveraging the natural language processing capabilities of BERT and GPT for sentiment analysis and market behavior prediction, combined with the time-series forecasting strength of the Prophet algorithm, this study aims to provide a robust framework for analyzing blockchain-driven financial markets. Furthermore, the integration of finance investment strategies ensures practical applicability by aligning machine learning insights with real-world investment decision-making processes. The proposed approach demonstrates potential for optimizing portfolio management, enhancing risk mitigation, and improving strategic investment in blockchain ecosystems. This work bridges the gap between cutting-edge machine learning technologies and financial innovation, offering valuable insights for researchers and practitioners in both domains.

Xiaohui Hu, Hang Feng, Pengcheng Xia, Gareth Tyson · 7 authors

The Web3 ecosystem is increasingly evolving to multi-chain, with decentralized applications (dApps) distributing across different blockchains, which drives the need for cross-chain bridges for blockchain interoperability. However, it further opens new attack surfaces, and media outlets have reported serious attacks related to cross-chain bridges. Nevertheless, few prior research studies have studied cross-chain bridges and their related transactions, especially from a security perspective. To fill the void, this paper presents the first comprehensive analysis of cross-chain transactions. We first make efforts to create by far the largest cross-chain transaction dataset based on semantic analysis of popular cross-chain bridges, covering 13 decentralized bridges and 7 representative blockchains, with over 80 million transactions in total. Based on this comprehensive dataset, we present the landscape of cross-chain transactions from angles including token usage, user profile and the purposes of transactions, etc. We further observe that cross-chain bridges can be abused for malicious/aggressive purposes, thus we design an automated detector and deploy it in the wild to flag misbehaviors from millions of cross-chain transactions. We have identified hundreds of abnormal transactions related to exploits and arbitrages, etc. Our research underscores the prevalence of cross-chain ecosystems, unveils their characteristics, and proposes an effective detector for pinpointing security threats.

Ting Li, Anfeng Liu, Shaobo Zhang, Tian Wang · 5 authors

No abstract is available for this record.

Xu Liu, Junwu Zhu

Smart contracts , being security-critical code, facilitate consensus among players and ensure secure and accurate value transfer, and formal verification is necessary to guarantee functional correctness of contracts. Game theory serves as one of the tools in formal verification by assessing whether the outcomes of contract executions meet the expected goals. While most studies employing game theory to verify smart contract functionality assume rational players, in practice, players may invoke and deploy smart contracts involving irrational behavior, casting doubt on the correctness of verification results. The aim of this study is to propose an alternative game model to verify smart contract functionality in dynamic player interactions where irrational behavior is involved. Specifically, a belief-based smart contract execution game (BSC-game) model was introduced, utilizing belief – the probability that a player believes in the irrationality of others – to capture how the irrational behavior of others affects a player’s contract execution decisions. Reasonable economic incentives were introduced to encourage honest behavior of players. Moreover, a computationally feasible method was designed to update players’ beliefs in large-scale dynamic smart contract executions. Theoretical analysis discloses the existence of equilibrium in the BSC-game, as well as the conditions for the number of faulty players within the system’s fault tolerance . We conducted the simulation experiments, and verified the business-oriented smart contract written in G language by the BSC-game model. The results further indicate that although players’ beliefs impact their decisions to execute contracts, reasonable economic incentives can motivate players to execute contracts honestly. This ensures that smart contract functionality aligns with expected goals, showing that the BSC-game model can verify and guarantee the correctness of contract functions. This new approach significantly contributes to bolstering smart contract security and credibility, positively influencing blockchain stability.

R. Revathi, Kamal Sharma, Gundavarapu Mallikarjuna Rao, Ammar Hameed Shnain · 6 authors

Although DeFi has been presented as the new generation of financial systems that cuts across all centralized financial systems, it is an innovation based on blockchain technology, which establishes global and open financial platforms of decentralized permissionless finance. Nonetheless, DeFi ecosystems have high levels of fragmentation, which creates difficulties for the unification of different protocols and blockchains to cooperate and communicate with each other. Subsequently, Graph Neural Networks (GNNs) have garnered considerable attention as a promising approach to model feature-interaction and extract insights from graph-structured data. In this paper, we introduce the Interoperable GNN DeFi Framework, which adapts the use of GNNs to work as a solution to the interoperability concern in Defi ecosystems. The following are the main components of the proposed system: graph representation, data integration, GNN model, interface and interoperability, monitoring and messaging, scalability optimization, and security considerations. Under the mentioned framework, positive results are notable for several scopes of interoperability, such as cross-chain operations with tokens, compatibility with other protocols, provision of liquidity, and risk tackling. The results shown below explain how much the efficiency, expandability, durability, and accessibility in the decentralized finance could be improved while using the GNN-based approaches. Future work in this topic entails the progression of the prior areas of research such as scalability, dynamics modeling, interdisciplinary collaboration, standardization, and security focusing on the continuing enhancement of interoperability opportunities in decentralized finance.

Roozbeh Sarenche, Alireza Aghabagherloo, Svetla Nikova⋆, Bart Preneel

The security of Bitcoin protocols is deeply dependent on the incentives provided to miners, which come from a combination of block rewards and transaction fees. As Bitcoin experiences more halving events, the protocol reward converges to zero, making transaction fees the primary source of miner rewards. This shift in Bitcoin's incentivization mechanism, which introduces volatility into block rewards, leads to the emergence of new security threats or intensifies existing ones. Previous security analyses of Bitcoin have either considered a fixed block reward model or a highly simplified volatile model, overlooking the complexities of Bitcoin's mempool behavior. This paper presents a reinforcement learning-based tool to develop mining strategies under a more realistic volatile model. We employ the Asynchronous Advantage Actor-Critic (A3C) algorithm, which efficiently handles dynamic environments, such as the Bitcoin mempool, to derive near-optimal mining strategies when interacting with an environment that models the complexity of the Bitcoin mempool. This tool enables the analysis of adversarial mining strategies, such as selfish mining and undercutting, both before and after difficulty adjustments, providing insights into the effects of mining attacks in both the short and long term. We revisit the Bitcoin security threshold presented in the WeRLman paper and demonstrate that the implicit predictability of valuable transaction arrivals in this model leads to an underestimation of the reported threshold. Additionally, we show that, while adversarial strategies like selfish mining under the fixed reward model incur an initial loss period of at least two weeks, the transition toward a transaction-fee era incentivizes mining pools to abandon honest mining for immediate profits. This incentive is expected to become more significant as the protocol reward approaches zero in the future.

Ming-Che Lee

This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. The significance of this research lies in integrating moving average technical indicators with deep learning models to enhance sensitivity to market momentum, and in normalizing these indicators to accurately reflect market trends and reversals. Utilizing historical OHLCV data along with four key technical indicators (SMA, EMA, TEMA, and MACD), the models classify trends into uptrend, downtrend, and neutral categories. Experimental results demonstrate that the inclusion of technical indicators, particularly MACD, significantly improves prediction accuracy. Furthermore, the Attention-GRU model offers computational efficiency suitable for real-time applications, while the Attention-LSTM model excels in capturing long-term dependencies. These findings contribute valuable insights for financial forecasting, providing practical tools for cryptocurrency traders and investors.

Chon Kit Lao, Sophie Zhou, Luyao Zhang, Fan Zhang · 5 authors

Blockchain systems such as Bitcoin and Ethereum have limitations in efficiency, resulting in an inability to immediately confirm all transactions, leading to extended periods of transactions residing in the mempool. We refer to these transactions as “long latency trans- actions” and this paper explores the issue of resource utilization in- efficiencies issues from these transactions. Utilizing the Geth client, the study quantifies the impact of these transactions on Ethereum’s resource consumption, which encompassing three crucial metrics: computational power, memory storage, and network bandwidth. Furthermore, this study also identifies three primary factors con- tributing to long latency transactions: low gas prices, long block processing times, and future-index transactions. Through empirical analysis, this study offers insights into the transaction-handling mechanisms in Ethereum. The implications of our findings aim to contribute to the enhancement of resource efficiency within the Ethereum blockchain ecosystem.

Ali Nikhalat-Jahromi, Ali Mohammad Saghiri, Mohammad Reza Meybodi

No abstract is available for this record.

Chang Liu, Zhili Wang, Qun Zhang, Shaoyong Guo · 5 authors

Data trading is a crucial means of unlocking the value of Internet of Things (IoT) data. However, IoT data differs from traditional material goods due to its intangible and replicable nature. This difference leads to ambiguous data rights, confusing pricing, and challenges in matching. Additionally, centralized IoT data trading platforms pose risks such as privacy leakage. To address these issues, we propose a profit-driven distributed trading mechanism for IoT data. First, a blockchain-based trading architecture for IoT data, leveraging the transparent and tamper-proof features of blockchain technology, is proposed to establish trust between data owners and data requesters. Second, an IoT data registration method that encompasses both rights confirmation and pricing is designed. The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data. For pricing, we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network. Finally, an IoT data matching method is designed based on the Stackelberg game. This establishes a Stackelberg game model involving multiple data owners and requesters, employing a hierarchical optimization method to determine the optimal purchase strategy. The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated. Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.

Muhammad Muzammil, Zhengyu Wu, Aruna Balasubramanian, Nick Nikiforakis

Ethereum Name Service (ENS) domains allow users to map human-readable names (such as gold.eth) to their cryptocurrency addresses, simplifying cryptocurrency transactions. Like traditional DNS domains, ENS domains must be periodically renewed. Failure to renew leads to expiration, making them available for others to register (a phenomenon known as dropcatching). This presents a security risk where attackers can register expired domains to leverage the residual trust associated with them and, in the context of ENS, receive transactions intended for their previous owners. In this paper, we conduct the first large-scale study on dropcatching in ENS domains. We curate and analyze a dataset comprising 3.1M ENS domains and 9.7M Ethereum transactions, finding that 241K of these domains were re-registered by new owners after expiration. Our findings indicate a preference for domains linked to high-income wallets in re-registrations. We identify 2,633 transactions that were misdirected to new owners, averaging the equivalent of thousands of US dollars. Lastly, we highlight the lack of countermeasures by digital wallet providers, and suggest straightforward approaches that they can use to minimize financial losses due to ENS dropcatching.

Ping Han, Hui Chen, Abdur Rasool, Qingshan Jiang · 5 authors

Bitcoin’s volatile nature has made its price prediction a sought-after mathematical model in the FinTech industry. Existing studies, however, need to look into the critical aspect of time-lagged sentiment in Bitcoin price forecasting. This omission is significant because time-lagged sentiment captures delayed market reactions that are not immediately apparent in price movements. Moreover, the correlation between time-lagged sentiment and technical indicators and the limitations of individual machine learning and deep learning models necessitates a comprehensive approach for accurate and reliable Bitcoin price predictions. This paper introduces the multimodal fusion Bitcoin (MFB), an innovative generalized multimodal fusion approach that effectively integrates BiLSTM and BiGRU layers for complex feature extraction. The model employs the BorutaShap algorithm for feature selection and utilizes attention mechanisms and spatial dropout for optimization and generalization. MFB’s training and validation use news and tweet data combined with Bitcoin technical indicators to explore the impact of time-lagged sentiment on price movements, leading to more accurate and timely market predictions. The MFB performs superior Bitcoin prediction performance, achieving 97.63% accuracy and an MAE of 0.0065. Experiments highlight MFB’s capability to outperform existing models, offering significant insights for investors in making informed decisions. MFB’s innovative methodology, particularly in next-hour Bitcoin price forecasting, marks an advancement in financial forecasting. By capturing the nuanced dynamics of market sentiment and its delayed effects, MFB is a pioneering multimodal fusion approach in the FinTech domain, revolutionizing Bitcoin price prediction.

Anjali Arora, Mayank Aggarwal

No abstract is available for this record.

Vid Keršič, Sašo Karakatič, Muhamed Turkanović

Zero-knowledge proofs introduce a mechanism to prove that certain computations were performed without revealing any underlying information and are used commonly in blockchain-based decentralized apps (dapps). This cryptographic technique addresses trust issues prevalent in blockchain applications, and has now been adapted for machine learning (ML) services, known as Zero-Knowledge Machine Learning (ZKML). By leveraging the distributed nature of blockchains, this approach enhances the trustworthiness of ML deployments, and opens up new possibilities for privacy-preserving and robust ML applications within dapps. This paper provides a comprehensive overview of the ZKML process and its critical components for verifying ML services on-chain. Furthermore, this paper explores how blockchain technology and smart contracts can offer verifiable, trustless proof that a specific ML model has been used correctly to perform inference, all without relying on a single trusted entity. Additionally, the paper compares and reviews existing frameworks for implementing ZKML in dapps, serving as a reference point for researchers interested in this emerging field. • An analytical and synthetic review of core on-chain ZKML concepts, supported by an extensive examination of both white and grey literature, establishing a foundational understanding of the field. • Through a detailed analysis, modelling, and descriptive approaches, the paper outlines the processes integral to on-chain ZKML. The study is focused on two distinct frameworks – EZKL and Orion , highlighting the differences between the two approaches, as well as the difference between the underlying ZKP systems, where the former framework is based on zk-SNARKs and the latter on zk-STARKs. • A laboratory experiment, coupled with a comparative analysis and use case execution comparison, was conducted to implement basic neural networks (NNs) across the two chosen frameworks, highlighting their capabilities and limitations in supporting on-chain ZKML.

Vasileios Moustakidis, Nikolaos Passalis, Anastasios Tefas

No abstract is available for this record.

Maria-Victoria Vladucu, Hailun Wu, Jorge Medina, Khondaker M. Salehin · 6 authors

Blockchain has emerged as a solution for ensuring accurate and truthful environmental variable monitoring needed for the management of pollutants and natural resources. The immutability property of blockchain helps protect the measured data on pollution and natural resources to enable truthful reporting and effective management and control of polluting agents. However, specifics on what to measure, how to use blockchain, and highlighting which blockchain frameworks have been adopted need to be explored to fill the research gaps. Therefore, we review existing works on the use of blockchain for monitoring and managing environmental variables in this paper. Specifically, we examine existing blockchain applications on greenhouse gas emissions, solid and plastic waste, food waste, food security, water usage, and the circular economy and identify what motivates the adoption of blockchain, features sought, used blockchain frameworks and consensus algorithms, and the adopted supporting technologies to complement data sensing and reporting. We conclude the review by identifying practical works that provide implementation details for rapid adoption and remaining challenges that merit future research.

Seungwoo Jung, Yeonho Yoo, Gyeongsik Yang, Chuck Yoo

Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability resorts to try-and-error. A key challenge is that BaaS is often perceived as a ``black-box,'' leading to uncertainties in performance and resource provisioning. Previous studies attempted to address this challenge; however, the impacts of both vertical and horizontal scaling remain elusive. To this end, we present machine learning-based models to predict network reliability and throughput based on scaling configurations. In our evaluation, the models exhibit prediction errors of ~1.9%, which is highly accurate and can be applied in the real-world.

Shiwen Zhang, Zhixue Li, Wei Liang, Kuan‐Ching Li · 5 authors

Mobile Crowd Sensing (MCS) has emerged as a new sensing paradigm due to its cost efficiency, mobility, and expandability. However, user selection for task allocation is a significant challenge in MCS. Most previous studies concentrate on two selection modes, opportunistic and participatory selection. Recent research has proposed a hybrid user selection mode that combines both advantages. However, existing hybrid user selection systems all rely on a centralized architecture, which is vulnerable to malicious attacks, and they do not consider the reliability of users and data availability. Moreover, they cannot ensure the individual rationality of users. To overcome these shortcomings, we propose a blockchain-based hybrid reliable user selection scheme for task allocation in MCS. Specifically, we replace the traditional central server with the blockchain and handle various sensing task operations using smart contracts on the blockchain to ensure system reliability and security. In addition, we design a user reputation calculation algorithm based on semi-Markov and a sensing data anomaly detection algorithm based on Long Short-Term Memory (LSTM) to ensure user reliability and data availability, and also a novel hybrid user selection algorithm, especially in the participatory user selection stage, where we use a user selection algorithm based on reverse auction to ensure the individual rationality of each user. Experimental results demonstrate the effectiveness of the proposed scheme through simulation experiments on GeoLife and sound-sensing public datasets.

Sheng Zhong, Abdullah Mueen

In the Bitcoin blockchain, an entity (e.g., a gambling service) may control multiple distinct address clusters. Links (i.e., trust relationships) between these disjoint address clusters can be established when one cluster is abandoned, and a new one is formed shortly thereafter. To link the clusters across time, we have developed a deep neural network model that exploits these synchronous actions derived from unlabeled data in a self-supervised manner. This model assesses whether two clusters exhibit synchronous temporal signatures indicative of a shared entity ownership.

Soumya Bajpai, Kapil Sharma, Brijesh Kumar Chaurasia

An Internet of Things (IoT)-enabled blockchain helps to ensure quick and efficient immutable transactions. Low-power IoT integration with the Bitcoin network has created new opportunities and difficulties for blockchain transactions. Utilising data gathered from IoT-enabled devices, this study investigates the application of ML regression models to analyse and forecast Bitcoin transaction patterns. Several ML regression algorithms, including Lasso Regression, Gradient Boosting, Extreme Boosting, Extra Tree, and Random Forest Regression, are employed to build predictive models. These models are trained using historical Bitcoin transaction data to capture intricate relationships between various transaction parameters. To ensure model robustness and generalisation, cross-validation techniques and hyperparameter tuning are also applied. The empirical results show that the Bitcoin cost prediction of blockchain transactions in terms of time series. Additionally, it highlights the possibility of fusing block- chain analytics with IoT data streams, illuminating how new technologies might work together to enhance financial institutions.

Zhuochen Fan, Xiangyuan Wang, Xiaodong Li, Jiarui Guo · 11 authors

In this paper, we study steady flows in data streams, which refers to the flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch-based algorithm, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the memory requirement by 75%. Our theoretical proofs show that the negative impact of the reborn technique is small. Experimental results show that, compared with the two comparison schemes, SteadySketch improves the Precision Rate (PR) by around 79.5% and 82.8%, and reduces the Average Relative Error (ARE) by around$905.9\times $and$657.9\times $, respectively. Finally, we provide three concrete cases: cache prefetch, Redis and P4 implementation. As we will demonstrate, SteadySketch can effectively improve the cache hit ratio while achieving satisfying performance on both Redis and Tofino switches. All related codes of SteadySketch are available at GitHub.

Behkish Nassirzadeh, Albert Heinle, Stefanos Leonardos, Anwar Hasan · 5 authors

Blockchain integration in industries like online advertising is hindered by its connectivity limitations to off-chain data. These industries heavily rely on precise counting systems for collecting and analyzing off-chain data. This requires mechanisms, often called oracles, to feed off-chain data into smart contracts. However, current oracle solutions are ill-suited for counting systems since the oracles do not know when to expect the data, posing a significant challenge. To address this, we present CountChain, a decentralized oracle network for counting systems. In CountChain, data is received by all oracle nodes, and any node can submit a proposition request. Each proposition contains enough data to evaluate the occurrence of an event. Only randomly selected nodes participate in a game to evaluate the truthfulness of each proposition by providing proof and some stake. Finally, the propositions with the outcome of True increment the counter in a smart contract. Thus, instead of a contract calling oracles for data, in CountChain, the oracles call a smart contract when the data is available. Furthermore, we present a formal analysis and experimental evaluation of the system's parameters on over half a million data points to obtain optimal system parameters. In such conditions, our game-theoretical analysis demonstrates that a Nash equilibrium exists wherein all rational parties participate with honesty.

Purnendu Bikash Acharjee

More and more in healthcare is blockchain technology applied for safe and open data storage. Still, it is understudied how deeply regression analysis combined with nonlinear dynamics into distributed ledger systems performs. This kind of approach may help to increase data transfer efficiency and help storage management in blockchain systems. Data speed and storage efficiency restrictions make current blockchain systems difficult to handle for large amounts of healthcare data. Conventional methods find poor data retrieval and transfer due to the great complexity and nonlinear characteristics of healthcare data. Combining nonlinear dynamics with deep regression analysis, this paper proposes a fresh approach for maximizing data transfer and storage in blockchain systems. Inspired by nonlinear dynamics ideas, a deep regression model aimed at maximizing block storage and forecast data transmission requirements was assessed on a simulated healthcare dataset using a distributed ledger system with 1,000 blocks and a 500 GB total dataset size. Performance criteria covered transmission efficiency and storage consumption. The proposed technique improved data transmission efficiency by thirty percent over current techniques. Another clear improvement was using storage; block size needs fell 25%. The best model, according to numerical research, lowered an average transmission time from 120 to 84 minutes and storage overhead from 200 to 150 GB.