Decentralized Finance (DeFi), propelled by Blockchain technology, has revolutionized traditional financial systems, improving transparency, reducing costs, and fostering financial inclusion. However, transaction activities i n these systems fluctuate significantly and the throughput can be effected. To address this issue, we propose a Dynamic Mining Interval (DMI) mechanism that adjusts mining intervals in response to block size and trading volume to enhance the transaction throughput of Blockchain platforms. Besides, in the context of public Blockchains such as Bitcoin, Ethereum, and Litecoin, a shift towards transaction fees dominance over coin-based rewards is projected in near future. As a result, the ecosystem continues to face threats from deviant mining activities such as Undercutting Attacks, Selfish Mining, and Pool Hopping, among others. In recent years, Dynamic Transaction Storage (DTS) strategies were proposed to allocate transactions dynamically based on fees thereby stabilizing block incentives. However, DTS’ utilization of Merkle tree leaf nodes can reduce system throughput. To alleviate this problem, in this paper, we propose an approach for combining DMI and DTS. Besides, we also discuss the DMI selection mechanism for adjusting mining intervals based on various factors.
Nir Chemaya, Lin William Cong, Emma Jorgensen, Dingyue Liu · 5 authors
Decentralized Finance (DeFi) is reshaping traditional finance by enabling direct transactions without intermediaries, creating a rich source of open financial data. Layer 2 (L2) solutions are emerging to enhance the scalability and efficiency of the DeFi ecosystem, surpassing Layer 1 (L1) systems. However, the impact of L2 solutions is still underexplored, mainly due to the lack of comprehensive transaction data indices for economic analysis. This study bridges that gap by analyzing over 50 million transactions from Uniswap, a major decentralized exchange, across both L1 and L2 networks. We created a set of daily indices from blockchain data on Ethereum, Optimism, Arbitrum, and Polygon, offering insights into DeFi adoption, scalability, decentralization, and wealth distribution. Additionally, we developed an open-source Python framework for calculating decentralization indices, making this dataset highly useful for advanced machine learning research. Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.
Decision-making and consensus in traditional blockchain protocols is formulated as a repeated Bernoulli trial that solves a computationally-intense lottery puzzle, called Proof-of-Work (PoW) in Bitcoin. This approach has shown robustness through practice, but does not scale with increasing network size and generation of new transactions. Resource constrained Internet of Things (IoT) networks are incompatible with full computation of schemes like Bitcoin's PoW. Our effort proposes a first step towards an alternative consensus using machine learning-based decision-making with prediction of fraud transactions to alleviate need for intense computation. To improve base approval probabilities for fraud detection in an ideal security setting, Vector GAN (VecGAN) is proposed to augment blockchain data in classifier training, which combines error-driven learning with Bayesian estimation to alleviate calculations. This two-step approach with augmentation and classification on new transactions is proposed as a novel approach to blockchain decision-making. Experimental prediction accuracy using VecGAN improved up to 3% on simplistic classifiers compared to other state-of-the-art augmentation techniques. Resource consumption in a realistic blockchain setting was reduced while improving block throughput by 50% compared to PoW. Future work will explore Sybil-spam defensive measures for realistic protocol implementation with this approach.
This research employs a Selective Neural Network Ensemble driven by Genetic Algorithms, employing an Artificial Neural Network ensemble methodology. The ensemble incorporates base model of any neural network is the multi-layered perceptron. Aim to explore the correlation between Bitcoin's features and its subsequent day's price movement. Leveraging approximately 200 cryptocurrency attributes over a two-year span, the ensemble predicts the direction of Bitcoin's price the following day, aiming to assess its practicality and relevance in real-world scenarios. In a comparative the ensemble-based trading strategy is evaluated using back-testing analysis over a 50-day period versus a “prior day trend“ trading approach. The former approach demonstrated noteworthy results, offering insights into its potential effectiveness. The best data range for training a Bitcoin price prediction model is determined by applying financial terms and methods, such as Simple Moving Average and Exponential Moving Average, as described in the article. A Linear Regression Model addresses the problem of choosing the appropriate dataset for improved forecasting results, achieving a high 97% prediction accuracy by adhering to the model's recommended data piece.
Blockchains are becoming increasingly important in today’s Internet, enabling large-scale decentralized applications with strong security and transparency properties. In a blockchain system, participants maintain and update the server-side state of an application by appending data as blocks onto an immutable, distributed ledger through a consensus protocol within a peer-to-peer network. There has been a significant increase in profit in mining blocks. For instance, Bitcoin miners currently receive over USD 200,000 per mined block. An essential determinant of these rewards is the time it takes to disseminate newly mined blocks across the network. This paper addresses the challenge of optimizing mining rewards by exploring topology design in a wide-area blockchain network utilizing a Proof-of-Work consensus protocol. We show that under low block times, the geographical location of a miner critically impacts the number of successful blocks mined by the miner. We also show that a miner may improve its success rate by increasing its connectivity to the network. However, contrary to the general wisdom that a faster network is always better for a miner, we show that increasing network connectivity (e.g., by adding more neighbors) is beneficial to a miner only up to a point after which the miner’s rewards degrade. This is because when a miner improves its connectivity, it inadvertently also aids other miners in increasing their connectivity. We also present a network-level collusion attack in which a miner can increase its block success rate by becoming part of a tightly connected cluster. Here too, we observe that the mining gains obtained increase with cluster size only up to a point, and decrease thereafter. Our findings highlight that the network topology is a key variable affecting miner performance in PoW blockchains that must not be overlooked. We demonstrate our observations via detailed simulations modeled using real-world measurement data.
Mohamed El Badaoui, Brahim Raouyane, Samira El Moumen, Mostafa Bellafkih
This study assesses the effectiveness of machine learning (ML) models in predicting fluctuations in the direction of Bitcoin prices using conventional technical indicators. Given the volatile nature of Bitcoin markets, achieving precise predictions is of utmost importance. The study improves machine learning models including logistic regression (LR), decision trees (DT), random forests (RF), support vector machines (SVM), Xgboost, and artificial neural networks (ANN) by integrating established technical indicators. By employing Optuna for hyperparameter optimization and the 'Time Series Split' technique for cross-validation, the study optimizes the models for time series data. The results demonstrate that ML models, when integrated with technical indicators, significantly outperform predictions based solely on those indicators. Evaluation metrics include accuracy, recall, and the F1 score. Through the incorporation of signal indicators, ML models offer a robust approach to predicting changes in Bitcoin prices. The proposed ML approach achieves a test accuracy exceeding 65.15%, highlighting the potential synergy between traditional financial prediction tools and contemporary technology. This study underscores the potential effectiveness of combining ML models with technical indicators for forecasting Bitcoin price movements.
Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV), which undermines both the consensus security and efficiency of blockchain systems. The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction. In this paper, we conduct the first systematic study on DeFi MEV activities in Flashbots bundle by developing ActLifter, a novel automated tool for accurately identifying DeFi actions in transactions of each bundle, and ActCluster, a new approach that leverages iterative clustering to facilitate us to discover known/unknown DeFi MEV activities. Extensive experimental results show that ActLifter can achieve nearly 100% precision and recall in DeFi action identification, significantly outperforming state-of-the-art techniques. Moreover, with the help of ActCluster, we obtain many new observations and discover 17 new kinds of DeFi MEV activities, which occur in 53.12% of bundles but have not been reported in existing studies.
Abstract With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction’s confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network ( Hybrid-CTEN ), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed.
Mihailo Todorović, Aleksandar Petrović, Ana Toskovic, Miodrag Živković · 6 authors
This study focuses on analyzing historical data to forecast future trends in Bitcoin prices due to its influence on the business landscape. Its high volatility attracted attention to understanding the influencing factors for its price. This paper presents an empirical investigation using time-series data of various exogenous and endogenous variables. Closing prices of Bitcoin and Ethereum, along with the daily volume of Bitcoin-related tweets are examined for Bitcoin closing price prediction by a long-short term memory (LSTM) network, fine-tuned by a hybrid adaptive reptile search algorithm. The analysis covers a three-year period, in which data is divided into training, validation, and testing sets. Comparative analysis against LSTM networks tuned by other high-performing metaheuristic algorithms demonstrates that the novel approach outperforms competitors in terms of standard regression metrics.
Muhammad Haziq Abdul Hadi, Nor Azuana Ramli, Qamar UI Islam
Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting.
Kawsalya Maharajan, A. V. Senthil Kumar, Ibrahiem M. M. El Emary, Priyanka Sharma · 9 authors
Blockchain encourages artificial intelligence towards intelligence while also increasing its autonomy and credibility. In this chapter, the authors examine the relationship between blockchain technology and artificial intelligence from a more thorough and three-dimensional standpoint. One of the greatest problems with blockchain implementations in IoV is that they cannot meet the computational and energy needs of conventional blockchain systems since IoV nodes are limited in their ability to use resources. A marketplace that enables stakeholders (CSPs, asset suppliers, service providers, regulators, etc.) to interact and exchange value with confidence based on smart provenance and governance may be developed using blockchain and distributed ledger technologies (DLT). These innovations offer a decentralised audit architecture that is safe. Such transactions (who uses what) can be kept on a distributed ledger marketplace in an immutable setting. A decentralised consensus process that does not need mining or incentivization in a permissionless architecture ensures data integrity.
Consensus algorithms are the core technology of a blockchain and directly affect the implementation and application of blockchain systems. Delegated proof of stake (DPoS) significantly reduces the time required for transaction verification by selecting representative nodes to generate blocks, and it has become a mainstream consensus algorithm. However, existing DPoS algorithms have issues such as "one ballot, one vote", a low degree of decentralization, and nodes performing malicious actions. To address these problems, an improved DPoS algorithm based on community discovery is designed, called CD-DPoS. First, we introduce the PageRank algorithm to improve the voting mechanism, achieving "one ballot, multiple votes", and we obtain the reputation value of each node. Second, we propose a node voting enthusiasm measurement method based on the GN algorithm. Finally, we design a comprehensive election mechanism combining node reputation values and voting enthusiasm to select secure and reliable accounting nodes. A node credit incentive mechanism is also designed to effectively motivate normal nodes and drive out malicious nodes. The experimental simulation results show that our proposed algorithm has better decentralization, malicious node eviction capabilities and higher throughput than similar methods.
The application of blockchain technology in the Internet of vehicles has becomes an effective solution. Blockchain's consensus process is a key component that has a direct impact on the reliability and security of on-chain data. The design of the consensus mechanism is an efficient technique to encourage the integration of blockchain into IoV. Traditional Proof of Work and Proof of Stake consensus relies on the competition while wasting the majority of resources or may lead to unfair circumstances. Practical Byzantine Fault Tolerance consensus method has been proved to adapt to IoV well while rotation of primary node selection seems not suitable for IoV environment. Data in the Internet of Vehicles is highly regional and consensus with IoV is expected to be adapt to those specific data. To address the aforementioned issues to some extent, this paper suggests a consensus mechanism called Proof of Traffic Condition consensus. In order to better reflect the road data distribution, the proposed consensus mechanism suggests a new primary node election procedure based on road and historical on-chain information. Simulation experiments are carried out to prove the proposed consensus mechanism can effectively classify RSUs and appropriately describes roads.
Gas inefficiency in smart contracts deployed on the Ethereum blockchain can lead to unnecessary expenses due to high gas fees. In this article, we address this issue by proposing GasOptiScan, an innovative web application tool to automatically detect all the possible Fusible Loop Optimization (FLO) patterns considering multiple loops at the same time within gas-inefficient code present in smart contracts. The findings revealed that a range of operations, such as comparisons and conditional jumps, can be minimized to reduce the number of opcodes and the associated gas fees. GasOptiScan harnesses cutting-edge static code analysis techniques to equip developers with valuable fusible loop pattern recommendations to optimize gas usage and minimize transaction costs in smart contract execution. With GasOptiScan's insightful analysis, developers can make informed optimizations that result in potential cost savings, ultimately enhancing the overall performance and affordability of executing smart contracts on the Ethereum blockchain. By leveraging techniques such as hexadecimal conversion, graph analysis, block detection, and variable identification, the tool identifies fusible blocks with varying upper bounds and lower bounds to minimize gas fees during contract execution. The GasOptiScan tool is designed with user-friendliness in mind, ensuring a seamless and intuitive experience for developers and enabling them to optimize their smart contracts and maximize efficiency on the Ethereum blockchain.
The emergence of Web3 technologies has led to the development of decentralized funding platforms, which allow individuals and organizations to raise funds without the need for intermediaries such as banks and venture capitalists. The underlying technology powering these platforms is blockchain, known for its ability to offer a reliable and transparent method of recording transactions and overseeing financial operations. This research paper aims to explore the Web3 funding platform landscape, its benefits and challenges, and the potential impact it could have on traditional funding models. The paper also examines the key features and functionalities of these platforms, including smart contracts, tokenization, and decentralized governance. It also presents the design and development of a platform for investors to invest in startups using cryptocurrencies. The platform is designed to address the challenges faced by startups in accessing funding, while providing investors with new investment opportunities in the fast-growing world of cryptocurrencies. Through a review of relevant literature and case studies, this research paper provides insights into the current state of Web3 funding platforms, their adoption and growth trajectory, and the regulatory environment that surrounds them. Overall, the platform offers a new investment opportunity for investors, while also providing startups with access to much-needed funding. The platform has the potential to disrupt traditional funding models and provide a more efficient and transparent way for startups to access funding.
A blockchain is a distributed ledger composed of immutable blocks of data that often refer to money transfers. As blockchain networks gain popularity, there is a rising concern for security against malicious and hacking users. Detection anomalies and unusual account activities can be based on comparing upcoming activity with recent and historical data. However, the size and rapid growth of the complete blockchain history can result in slow and expensive processing. This paper proposes a solution to this challenge by analyzing summarized block data structures, known as sketches, instead of the entire blockchain. Sketches are commonly used in computer systems and blockchain networks to provide efficient query executions while maintaining a compact data representation. This study explores the use of sketches, such as Bloom Filter and HyperLogLog, to identify suspicious accounts without requiring the examination of the entire blockchain data. We design solutions for anomaly detection of certain goals that may be indications of known attacks. We develop methods to identify accounts with high transaction volume, frequency, and node degree. Furthermore, the innovation of this paper lies in the generalization of sketch-based anomaly detection through a generic solution capable of addressing diverse queries. We conduct experiments based on real Ethereum data and compare the accuracy, time complexity, and memory usage of our algorithms with traditional detection algorithms that rely on the complete blockchain data. Our results indicate that sketch-based anomaly detection methods can provide a practical and scalable solution for detecting anomalies in transactions on blockchain networks. We managed to reduce the amount of memory used by the detection process by 90%-96% and reduce the time complexity by 86% while maintaining high accuracy.
I.sibel KERVANCI, Mehmet Fatih Akay, Eren Özceylan
Bitcoin has high price fluctuations, which involve high risks and high return rates for investors. These high earnings have attracted the attention of investors. This paper proposes a new model for Bitcoin price prediction that effectively reduces prediction error. Hyperparameter optimization methods such as Bayesian optimization (BO), random search and grid search with Long Short-Term Memory (LSTM), Gated Repetitive Unit (GRU), and hybrid LSTM-GRU utilised. Models with BO achieved better results than others. To improve each model's results with BO; Gradient Incremental Regression Trees (GBRT), Gaussian Process (GP), Random Forest (RF) and Extra Trees (ET) were applied to optimizers and corresponding surrogate functions. Evaluating the effects of hyper-parameter values on the problem for each method contributes to the parameter selection process for similar prediction problems. To increase comparability in the literature, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Mean Square Error (MSE) were used. There is a least one hyper-parameter combination, which produces a result close to the best value for each model when the results obtained from the experiments are interpreted. BO with hybrid LSTM-GRU outperformed all methods in this paper and the examined literature for the value of RMSE, MSE, and MAE.
Kithmini Godewatte Arachchige, Philip Branch, Jason But
Blockchain technology is an information security solution that operates on a distributed ledger system. Blockchain technology has considerable potential for securing Internet of Things (IoT) low-powered devices. However, the integration of IoT and blockchain technologies raises a number of research issues. One of the most important is the energy consumption of different blockchain algorithms. Because IoT devices are typically low-powered battery-powered devices, the energy consumption of any blockchain node must be kept low. IoT end nodes are typically low-powered devices expected to survive for extended periods without battery replacement. Energy consumption of blockchain algorithms is an important consideration in any application that combines both technologies, as some blockchain algorithms are infeasible because they consume large amounts of energy, causing the IoT device to reach high temperatures and potentially damaging the hardware; they are also a possible fire hazard. In this paper, we examine the temperatures reached in devices used to process blockchain algorithms, and the energy consumption of three commonly used blockchain algorithms running on low-powered microcontrollers communicating in a wireless sensor network. We found temperatures of IoT devices and energy consumption were highly correlated with the temperatures reached. The results indicate that device temperatures reached 80 °C. This work will contribute to developing energy-efficient blockchain-based IoT sensor networks.
Jai Kishan Karahyla, Neelam Sharma, Sushant Chamoli, Dr Anil Shirgire · 6 authors
Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin’s pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN.
In this dissertation, I investigate the existing smart contract problems that limit cognitive abilities. I use Taylor's serious expansion, polynomial equation, and fraction-based computations to overcome the limitations of calculations in smart contracts. To prove the hypothesis, I use these mathematical models to compute complex operations of naive Bayes, linear regression, decision trees, and neural network algorithms on Ethereum public test networks. The smart contracts achieve 95\% prediction accuracy compared to traditional programming language models, proving the soundness of the numerical derivations. Many non-real-time applications can use our solution for trusted and secure prediction services.
This paper presents a general-purpose approach for the drug supply chain management, by proposing a DLT-based methodology to facilitate and make more efficient the development of such applications. For specific domains, such as drug management and traceability, a system based on Django Python framework, on Ethereum blockchain, and on web3.py library has been developed that can be customized for most real supply chains, automatically generating the specific applications (database schema, smart contracts, apps). A case study about a simple drug traceability system for a producer to hospital wards is described, to show how this approach works.