Abstract Nowadays, the prediction of cryptocurrency side effects on the critical aspects of the exchange rates in intelligent business is one of the main challenges in the financial market. Cryptocurrency is defined as a set of digital information concerning internal financial protocols of digital marketing, such as blockchain, which operates according to a decentralized architecture. On the other hand, fraud activities in Ethereum transfer and management of cryptocurrency now increase and affect safe transactional processes. This article presents a new machine‐learning approach to Ethereum fraud Detection based on Bayesian Optimizable Ensemble Bagged Trees (BOEBT) algorithm. Moreover, the main goal of this study is to derive the accuracy of the cryptocurrency prediction model using different machine‐learning algorithms and compare their evaluation parameters together. The performance of the proposed prediction model using the machine learning algorithms was evaluated by the MATLAB tool. The experimental results show that the proposed BOEBT algorithm merits achieving 99.21% accuracy and 99.14% F1‐Score to other machine learning algorithms for cryptocurrency fraud prediction.
This study proposes a framework to enhance privacy in Blockchain-based Internet of Things (BIoT) systems used in the healthcare sector. The framework addresses the challenge of leveraging health data for analytics while protecting patient privacy. To achieve this, the study integrates Differential Privacy (DP) with Federated Learning (FL) to protect sensitive health data collected by IoT nodes. The proposed framework utilizes dynamic personalization and adaptive noise distribution strategies to balance privacy and data utility. Additionally, blockchain technology ensures secure and transparent aggregation and storage of model updates. Experimental results on the SVHN dataset demonstrate that the proposed framework achieves strong privacy guarantees against various attack scenarios while maintaining high accuracy in health analytics tasks. For 15 rounds of federated learning with an epsilon value of 8.0, the model obtains an accuracy of 64.50%. The blockchain integration, utilizing Ethereum, Ganache, Web3.py, and IPFS, exhibits an average transaction latency of around 6 seconds and consistent gas consumption across rounds, validating the practicality and feasibility of the proposed approach.
With the continuous advancement of information technology, a growing number of works, including articles, paintings, and music, are being digitized. Digital content can be swiftly shared and disseminated via the Internet. However, it is also vulnerable to malicious plagiarism, which can seriously infringe upon the rights of creators and dampen their enthusiasm. To protect creators’ rights and interests, a sophisticated method is necessary to authenticate digital intellectual property rights. Traditional authentication methods rely on centralized, trustworthy organizations that are susceptible to single points of failure. Additionally, these methods are prone to network attacks that can lead to data loss, tampering, or leakage. Moreover, the circulation of copyright information often lacks transparency and traceability in traditional systems, which leads to information asymmetry and prevents creators from controlling the use and protection of their personal information during the authentication process. Blockchain technology, with its decentralized, tamper-proof, and traceable attributes, addresses these issues perfectly. In blockchain technology, each node is a peer, ensuring the symmetry of information. However, the transparent feature of blockchains can lead to the leakage of user privacy data. Therefore, this study designs and implements an Ethereum blockchain-based intellectual property authentication scheme with privacy protection. Firstly, we propose a method that combines elliptic curve cryptography (ECC) encryption with digital signatures to achieve selective encryption of user personal information. Subsequently, an authentication algorithm based on Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is adopted to complete the authentication of intellectual property ownership while encrypting personal privacy data. Finally, we adopt the InterPlanetary File System (IPFS) to store large files, solving the problem of blockchain storage space limitations.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
C. Li, Sidi Mohamed Beillahi, Guang Yang, Ming Wu · 6 authors
Authenticated storage access is the performance bottleneck of a blockchain, because each access can be amplified to potentially O (log n ) disk I/O operations in the standard Merkle Patricia Trie (MPT) storage structure. In this article, we propose a multi-Layer Versioned Multipoint Trie (LVMT), a novel high-performance blockchain storage with significantly reduced I/O amplifications. LVMT uses the authenticated multipoint evaluation tree vector commitment protocol to update commitment proofs in constant time. LVMT adopts a multi-layer design to support unlimited key–value pairs and stores version numbers instead of value hashes to avoid costly elliptic curve multiplication operations. In our experiment, LVMT outperforms the MPT in real Ethereum traces, delivering read and write operations 6× faster. It also boosts blockchain system execution throughput by up to 2.7×.
João Ricardo Pereira Marques, Jo�ão Fabrício Filho, R.A. Pereira Goncalves
Este artigo apresenta uma abordagem para a criação de um dataset com os códigos dos contratos implantados na rede Ethereum. O critério de seleção compreende os três tipos de transações: regulares, de criação e de execução do código dos contratos. As ferramentas ethereumetl e Duckdb são utilizadas para extração dos dados e a criação do banco de dados. A base de dados reúne informações de blocos, transações, código dos contratos, informações sobre as instruções utilizadas em cada contrato e quantas vezes cada contrato foi executado e em quais versões da Ethereum Virtual Machine (EVM).
Abstract. The purpose of a consensus protocol is to keep a distributed network of nodes “in sync,” even in the presence of an unpredictable communication network and adversarial behavior by some of the participating nodes. In the permissionless setting relevant to modern blockchain protocols, these nodes may be operated by a large number of unknown players, with each player free to use multiple identifiers and to start or stop running the protocol at any time. Establishing that a permissionless consensus protocol is “secure” thus requires both a distributed computing argument (that the protocol guarantees consistency and liveness unless the fraction of adversarial participation is sufficiently large) and an economic argument (that carrying out an attack would be prohibitively expensive for a potential attacker). There is a mature toolbox for assembling arguments of the former type; the goal of this paper is to lay the foundations for arguments of the latter type. For example, the Ethereum protocol is oft-claimed to be “more economically secure” after “the merge,” meaning in its current proof-of-stake incarnation relative to the (proof-of-work) original. What, formally, does this assertion mean? Is it true? Could there be alternative protocols that are “still more economically secure” than Ethereum? How do the answers depend on the assumptions imposed on, for example, the reliability of message delivery or the active participation of non-malicious players? An ideal permissionless consensus protocol would, in addition to satisfying standard consistency and liveness guarantees, render consistency violations prohibitively expensive for the attacker without collateral damage to honest participants—for example, by programatically confiscating an attacker’s resources without reducing the value of honest participants’ resources, as is the intention for slashing in a proof-of-stake protocol. We make this idea precise with our notion of the EAAC (expensive to attack in the absence of collapse) property and prove the following results: (1) In the synchronous and dynamically available setting (in which the communication network is reliable but nonmalicious players may be periodically inactive), with an adversary that controls at least one-half of the overall resources, no protocol can be EAAC. In particular, this result rules out EAAC for all typical longest-chain protocols (be they proof-of-work or proof-of-stake). (2) In the partially synchronous and quasi-permissionless setting (in which resource-controlling non-malicious players are always active but the communication network may suffer periods of unreliability), with an adversary that controls at least one-third of the overall resources, no protocol can be EAAC. In particular, slashing in a proof-of-stake protocol cannot achieve its intended purpose if message delays cannot be bounded a priori. (3) In the synchronous and quasi-permissionless setting, there is a proof-of-stake protocol with slashing that, provided the adversary controls less than two-thirds of the overall stake, satisfies the EAAC property. Thus, while only “classical security” is possible in the dynamically available or partially synchronous settings, proof-of-stake protocols with slashing can obtain additional “economic security” in the quasi-permissionless and synchronous settings. All three results are optimal with respect to the size of the adversary. With respect to Ethereum, our work formalizes the potential security benefits of proof-of-stake sybil-resistance coupled with slashing and the common belief that the merge has increased Ethereum’s economic security. Our work also provides mathematical justifications for several key design decisions behind the post-merge Ethereum protocol, ranging from long cooldown periods for unstaking to economic penalties for inactivity.
Yifan Mao, Mengya Zhang, Shaileshh Bojja Venkatakrishnan, Zhiqiang Lin
Maximal extractable value (MEV) in which block proposers unethically gain profits by manipulating the order in which transactions are included within a block, is a key challenge facing blockchains such as Ethereum today. Left unchecked, MEV can lead to a centralization of stake distribution thereby ultimately compromising the security of blockchain consensus. To preserve proposer decentralization (and hence security) of the blockchain, Ethereum has advocated for a proposer-builder separation (PBS) in which the functionality of transaction ordering is separated from proposers and assigned to separate entities called builders. Builders accept transaction bundles from searchers, who compete to find the most profitable bundles. Builders then bid completed blocks to proposers, who accept the most profitable blocks for publication. The auction mechanisms used between searchers, builders and proposers are crucial to the overall health of the blockchain. In this paper, we consider PBS design in Ethereum as a game between searchers, builders and proposers. A key novelty in our design is the inclusion of future block proposers, as all proposers of an epoch are decided ahead of time in proof-of-stake (PoS) Ethereum within the game model. Our analysis shows the existence of alternative auction mechanisms that result in a better (more profitable) equilibrium to players compared to state-of-the-art. Experimental evaluations based on synthetic and real-world data traces corroborate the analysis. Our results highlight that a rethinking of auction mechanism designs is necessary in PoS Ethereum to prevent disruption.
We present a formal specification for the implementation of Solana virtual machine (SVM) rollups deployed on top of the Solana Layer 1 (L1) blockchain. We further discuss our motivation, implementation, design decisions, limitations, and preliminary results. Overall, this paper is intended to serve as an initial introduction to building such system(s) on top of the Solana L1 blockchain, but does not represent an absolute. Lastly, we comment discuss on extensions of this specification to support SVM rollups on other well-established L1 blockchains systems such as Ethereum.
Sonal Sahu, Alejandro Fonseca Ramírez, Jong‐Min Kim
This paper investigates calendar anomalies, volatility patterns, and the best forecasting model for predicting volatility in the cryptocurrency market, focusing on ten prominent cryptocurrencies: Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Spanning from January 2016 to December 2023, the study utilizes sophisticated statistical models such as GARCH (p,q), EGARCH (p,q), and GJR-GARCH (p,q) to analyze precise changes in market dynamics and the impact of day-of-week fluctuations on cryptocurrency returns. Empirical evidence reveals significant findings regarding the persistence of volatility, positive and negative news effects on volatility, and day-of-week effects on cryptocurrency returns. Post-COVID-19, Sunday emerges as the least volatile day for cryptocurrencies, while Thursdays and Tuesdays exhibit greater volatility. Binance, Ethereum, Dogecoin, and Tether show anomalies where returns on Tuesday and Thursday significantly differed from those on other days of the week. Many other currencies, like the USD coin, Cardano, and Ripple, show anomalies only in the pre-COVID-19 period. The findings highlight the best forecast model for volatility for each top cryptocurrency, offering practical implications for investors, traders, regulators, and policymakers. These insights emphasize the importance of understanding and addressing calendar anomalies in the cryptocurrency market for informed decision-making, trading strategies, regulatory frameworks, and market stability.
The closed architecture of prevailing blockchain systems renders the usage of this technology mostly infeasible for a wide range of real-world problems. Most blockchains trap users and applications in their isolated space without the possibility of cooperating or switching to other blockchains. Therefore, blockchains need additional mechanisms for seamless communication and arbitrary data exchange between each other and external systems. Unfortunately, current approaches for cross-blockchain communication are resource-intensive or require additional blockchains or tailored solutions depending on the applied consensus mechanisms of the connected blockchains. Therefore, we propose an oracle with an off-chain aggregation mechanism based on Zero-Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) to facilitate cross-blockchain communication. The oracle queries data from another blockchain and applies a rollup-like mechanism to move state and computation off-chain. The zkOracle contract only expects the transferred data, an updated state root, and proof of the correct execution of the aggregation mechanism. The proposed solution only requires constant 378 kgas to submit data on the Ethereum blockchain and is primarily independent of the underlying technology of the queried blockchains.
Vilhelm Sjöberg, Kinnari Dave, Daniel Britten, Maria A. Schett · 9 authors
Programs executed on a blockchain - smart contracts - have high financial stakes; their correctness is crucial. We argue, that this correctness needs to be foundational: correctness needs to be based on the operational semantics of their execution environment. In this work we present a foundational system - the DeepSEA system - targeting the Ethereum blockchain as the largest smart contract platform. The DeepSEA system has a small but sufficiently rich programming language amenable for verification, the DeepSEA language, and a verified DeepSEA compiler. Together they enable true end-to-end verification for smart contracts. We demonstrate usability through two case studies: a realistic contract for Decentralized Finance and contract for crowdfunding.
Ethereum has become one of the primary global platforms for cryptocurrency, playing an important role in promoting the diversification of the financial ecosystem. However, the relative lag in regulation has led to a proliferation of malicious activities in Ethereum, posing a serious threat to fund security. Existing regulatory methods usually detect malicious accounts through feature engineering or large-scale transaction graph mining. However, due to the immense scale of transaction data and malicious attacks, these methods suffer from inefficiency and low robustness during data processing and anomaly detection. In this regard, we propose an Ethereum Transaction Graph Compression method named TGC4Eth, which assists malicious account detection by lightweighting both features and topology of the transaction graph. At the feature level, we select transaction features based on their low importance to improve the robustness of the subsequent detection models against feature evasion attacks; at the topology level, we employ focusing and coarsening processes to compress the structure of the transaction graph, thereby improving both data processing and inference efficiency of detection models. Extensive experiments demonstrate that TGC4Eth significantly improves the computational efficiency of existing detection models while preserving the connectivity of the transaction graph. Furthermore, TGC4Eth enables existing detection models to maintain stable performance and exhibit high robustness against feature evasion attacks.
Jason Al Hilal Sabda Dewa, Indra Waspada, Priyo Sidik Sasongko
In the rapidly evolving era of Decentralized Finance (DeFi), the convergence of Blockchain technology with intermediary-free financial services has forged a revolutionary landscape. However, this progress has been accompanied by critical challenges, notably the Single Key Risk and reentrancy attack threats against ERC20 smart contracts in private Ethereum Blockchain. This research formulated a proactive approach and implemented an innovative solution by embodying Reliable Decentralized Finance through the deployment of a 3-of-3 Hybrid Multisignature Wallet system with Withdrawal Pattern, External Effects, and Mutual Exclusion in the form of a Decentralized Application (DApps). The system not only applied withdrawal patterns but also integrated external effects and the principle of mutual exclusion to enhance the security of smart contracts. The system development methodology was executed comprehensively using Agile Software Engineering, encompassing the development of both smart contracts and external applications (decentralized applications). Testing was conducted using Ganache EVM (Ethereum Virtual Machine) connected to the Hot Wallet Metamask as an Externally Owned Account (EOA) for transaction signing. Valid results were obtained from comprehensive testing against the system's functional requirements, affirming the system's success in managing Single Key Risk and preventing reentrancy attacks, providing a reliable and concrete solution
The increasing number of distinct blockchains has led to a growing need for data exchange and asset transfer across various isolated blockchains. To address this, cross-chain bridges have emerged as a critical mechanism for enabling interoperability and facilitating data and asset exchange across diverse blockchains. Among these bridges, the Layer-0 bridge stands out as a scalability solution that enhances blockchain performance at the foundational layer of data transition, without altering the blockchain's structure. Stargate is a notable Layer-0 Lock-and-Unlock cross-chain bridge that supports transactions across various EVM-based blockchains, with the highest Total Value Locked (TVL) among cross-chain bridges of the same kind. While previous cross-chain research has primarily focused on Layer-2 bridges, this study specifically examines Stargate and analyzes its dynamics as well as potential vulnerabilities. We collect transaction data of Stargate on six blockchains including Ethereum, Polygon, Binance Smart Chain, Avalanche, Arbitrum and Optimism. Our findings reveal the transaction patterns and evidence of exploitations of Stargate by investigating its transaction dynamics over time.
In public blockchains, leaking secret keys can cause the permanent loss of crypto assets. It is imperative to understand the illicit activities on blockchains related to leaked keys. This paper presents the first measurement study that uncovers, quantifies, and characterizes the actual misuses of the leaked keys from top websites on the Internet to withdraw assets on Ethereum. By finding key-leaking web pages and joining them with transactions, the study reveals 7.29*10^6/0.59*10^6 USD worth of assets on Ethereum mainnet/Binance Smart Chain (BSC) are withdrawn from 1421/1514 leaked secret keys. Mitigations are proposed to avoid the financial loss caused by leaked keys.
Open access
User Authentication and Security Systems
Advanced Malware Detection Techniques
Advanced Steganography and Watermarking Techniques
Tanusree Sharma, Vivek Nair, Henry Wang, Yang Wang · 5 authors
Key management has long remained a difficult unsolved problem in the field of usable security. While password-based key derivation functions (PBKDFs) are widely used to solve this problem in centralized applications, their low entropy and lack of a recovery mechanism make them unsuitable for use in decentralized contexts. The multi-factor key derivation function (MFKDF) is a recently proposed cryptographic primitive that aims to address these deficiencies by incorporating commonly used authentication factors into the key derivation process. In this paper, we implement an MFKDF-based Ethereum wallet and perform a user study with 27 participants to directly compare its usability against traditional cryptocurrency wallet architectures. Our results show that MFKDF-based applications outperform conventional key management approaches on both subjective and objective metrics, with a 37% higher average SUS score (p < 0.0001) and 71% faster task completion times (p < 0.0001) for the MFKDF-based wallet.
The Internet of Things (IoT) refers to a complex network comprising interconnected devices that transmit their data via the Internet. Due to their open environment, limited computation power, and absence of built-in security, IoT environments are susceptible to various cyberattacks. Denial of service (DDoS) attacks are among the most destructive types of threats. The Multi-vector DDoS attack is a contemporary and formidable form of DDoS wherein the attacker employs a collection of compromised IoT devices as zombies to initiate numerous DDoS attacks against a target server. A Blockchain-based Operational Threat Intelligence framework, OTI-IoT, is proposed in this article to counter multi-vector DDoS attacks in IoT networks. A “Prevent-then-Detect” methodology was utilized to deploy the OTI-IoT framework in two distinct stages. During Phase 1, the consortium Blockchain network validators employ the IPS module, composed of a smart contract for attack prevention and access control, and Proof of Voting consensus, to thwart attacks. Validators are outfitted with deep learning-based IDS instances to detect multi-vector DDoS attacks during Phase 2. Alert messages are generated by the IDS module’s alert generation and propagation smart contract in response to identifying malicious IoT sources. The feedback loop from the IDS module to the IPS module prevents incoming traffic from malicious sources. The proposed OTI framework capabilities are realized as an outcome of combining and storing the outcomes of the IDS and IPS modules on the consortium Blockchain. Each validator maintains a shared ledger containing information regarding threat sources to ensure robust security, transparency, and integrity. The operational execution of OTI-IoT occurs on an individual Ethereum Blockchain. The empirical findings indicate that our proposed framework is most suitable for real-time applications due to its ability to lower attack detection time, decreased block validation time, and higher attack prevention rate.
Roger Lacson, Yufei Yu, Tsung-Ting Kuo, Lucila Ohno‐Machado
OBJECTIVE: Blockchain has emerged as a potential data-sharing structure in healthcare because of its decentralization, immutability, and traceability. However, its use in the biomedical domain is yet to be investigated comprehensively, especially from the aspects of implementation and evaluation, by existing blockchain literature reviews. To address this, our review assesses blockchain applications implemented in practice and evaluated with quantitative metrics. MATERIALS AND METHODS: This systematic review adapts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to review biomedical blockchain papers published by August 2023 from 3 databases. Blockchain application, implementation, and evaluation metrics were collected and summarized. RESULTS: Following screening, 11 articles were included in this review. Articles spanned a range of biomedical applications including COVID-19 medical data sharing, decentralized internet of things (IoT) data storage, clinical trial management, biomedical certificate storage, electronic health record (EHR) data sharing, and distributed predictive model generation. Only one article demonstrated blockchain deployment at a medical facility. DISCUSSION: Ethereum was the most common blockchain platform. All but one implementation was developed with private network permissions. Also, 8 articles contained storage speed metrics and 6 contained query speed metrics. However, inconsistencies in presented metrics and the small number of articles included limit technological comparisons with each other. CONCLUSION: While blockchain demonstrates feasibility for adoption in healthcare, it is not as popular as currently existing technologies for biomedical data management. Addressing implementation and evaluation factors will better showcase blockchain's practical benefits, enabling blockchain to have a significant impact on the health sector.
Rus Hertanto, Masruri Muchtar, Pardomuan Robinson Sihombing
Mata uang crypto menjadi sangat familiar dan booming dalam beberapa tahun terakhir. Ada yang menganggapnya sebagai mata uang, ada yang menganggapnya sebagai instrumen investasi, ada yang menganggapnya hanya sebuah scam masa kini yang terus diobrolkan di khalayak ramai. Harga crypto memang sangat volatile, untuk itu peneliti ingin mencari faktor-faktor apa saja yang memengaruhi harga crypto, khususnya dari variabel ekonomi makro yang ada. Data dalam penelitian ini diambil dari chart bulanan dari masing-masing variabel, dengan timeframe setiap bulan dari 2019 sampai 2023. Peneliti menggunakan random effect model dalam melakukan uji regresi, dengan hasilnya adalah variabel independen S&P500 dan harga Bitcoin memiliki pengaruh positif terhadap harga Ethereum dan BNB. Sedangkan tiga variabel sisanya yaitu IHSG, harga emas, dan harga minyak mentah tidak memiliki pengaruh yang signifikan. Hal itu mengindikasikan bahwa variabel ekonomi makro pada dasarnya tidak berpengaruh terhadap harga crypto. Pada akhirnya, yang menggerakkan harga crypto adalah permintaan dan penawaran dari pasar.
Verifiable Delay Function (VDF) is a cryptographic concept that ensures a minimum delay before output through sequential processing, which is resistant to parallel computing. One of the significant VDF protocols academically reviewed is the VDF protocol proposed by Pietrzak. However, for the blockchain environment, the Pietrzak VDF has drawbacks including long proof size and recursive protocol computation. In this paper, we present an implementation study of Pietrzak VDF verification on Ethereum Virtual Machine (EVM). We found that the discussion in the Pietrzak's original paper can help a clear optimization in EVM where the costs of computation are predefined as the specific amounts of gas. In our results, the cost of VDF verification can be reduced from 4M to 2M gas, and the proof length can be generated under 8 KB with the 2048-bit RSA key length, which is much smaller than the previous expectation.
Abstract: Eclipse attacks pose a serious threat to blockchain networks. Research has proven that Ethereum is more vulnerable to the Eclipse attack than the Bitcoin peer-to-peer network. Therefore, related research on Eclipse attacks on Ethereum is of great value. This paper proposes an improved XGBoost algorithm based on Bagging. It simulates a variety of random situations through the Bagging method, introduces randomness, reduces the risk of high errors, reduces the variance of the XGBoost model output, and improves the generalization ability of the model. It further enhances the model performance on binary classification problems and achieves efficient identification of Eclipse attack traffic and normal traffic.
This paper aims to explore the complex interrelationships between the prices of cryptocurrency, specifically Ethereum (ETH), and five top Non-Fungible Token (NFT) collections: Bored Ape Yacht Club, Mutant Ape Yacht Club, Azuki, Moonbirds, and Otherdeed. Motivated by the intertwining dynamics of these digital assets and the unexplored nature of their interdependencies, this study employs a Vector Autoregressive (VAR) model and utilizes Granger Causality to dissect the multifaceted interactions. The analysis period ranges from April 2021 to January 2023, a critical window of exponential growth and fluctuation in the digital asset market. The results demonstrate a statistically significant impact of ETH prices on NFT collection prices, but not vice versa, revealing the strong dependence of the NFT market on cryptocurrency volatility. Specifically, the research finds that changes in ETH’s value are predictive of shifts in NFT prices, whereas NFT price fluctuations lack predictive power for ETH prices. In conclusion, this research represents an advancement in understanding price dynamics in the rapidly evolving digital economy. By innovatively analyzing the co-movement of cryptocurrencies and NFTs, it not only enriches existing knowledge but also paves the way for further exploration, offering practical insights for diverse stakeholders navigating this exciting, ever-changing field.
Raed Alsini, Qasem Abu Al‐Haija, Abdulaziz A. Alsulami, Badraddin Alturki · 8 authors
Introduction: The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. Methods: In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. Results and discussion: To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.
Rui Dias, Mariana Chambino, Rosa Galvão, Paulo Alexandre · 5 authors
This study aimed to assess whether renewable energy cryptocurrencies such as Cardano (ADA), Ripple (XRP), IOTA (MIOTA), and Stellar (XLM) can be considered hedging assets and safe havens for cryptocurrencies classified as "dirty", such as Bitcoin Cash (BCH), Bitcoin (BTC) Litcoin (LTC), Ethereum (ETH), Ethereum Classic (ETC) and the clean energy stock indices WILDERHILL Clean Energy (ECO) and Clean Energy Fuels (CLNE), from July 6, 2018, to July 6, 2023. The results show that the movements decreased significantly during the Stress period, which includes the events of 2020 and 2022. The Cardano cryptocurrency shows moderate movements, indicating stability and diversification, while Stellar shows moderate movements that suggest resilience. Conversely, XRP shows varied movements, requiring some caution, while IOTA stands out for significant movements associated with sustainable assets. These results interest players operating in these markets when they want to diversify and rebalance their portfolios.