Consensus plays a crucial role in blockchain technology, with the deleted proof of stake (DPoS) consensus mechanism commonly utilized in both public and hybrid chains. However, the current DPoS mechanism faces challenges such as low node engagement in voting and potential security risks posed by malicious nodes. In response, we propose the DL-DPoS (deep link-delegated proof of stake) mechanism, which builds upon the DPoS framework. The DL-DPoS incorporates the LINK incentive mechanism to encourage inactive nodes to participate in voting and leader selection. Furthermore, a comprehensive credit scoring system based on wealth, performance, and stability is introduced to enhance the security of elected nodes. The verification process is optimized to involve all nodes except the leader node, and mechanisms are in place to handle malicious attacks by degrading or removing offending nodes and redistributing their responsibilities to the LINK group. Performance testing of the DL-DPoS mechanism, conducted through blockchain simulation tests using the GO language, shows a 23% increase in throughput compared to DPoS, with over 95% node participation and improved distribution of rights and equity. These results indicate the enhanced performance, security, and stability of the DL-DPoS consensus mechanism.
The amount of power required to mine one Bitcoin (BTC) can vary significantly depending on several factors, including the type of mining hardware being used, its efficiency, the cost of electricity, and the overall network difficulty at any given time. Mining BTC involves solving complex mathematical problems to validate transactions on the blockchain network, which requires significant computational power. This research paper focuses on dedicated mining machines, combining essential data and information into a singular comparison evaluation of these machines.
Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Constantinos Halkiopoulos
Big data and blockchain technology are coming together to revolutionize how decisions are made in a decentralized way across various industries. This review looks at how these technologies, along with distributed systems, can improve data security, transparency, and real-time processing, making decision-making more efficient and informed. The integration enhances data security with unchangeable records, increases transparency and traceability, and supports real-time data analysis. However, there are challenges to overcome, including scalability, data privacy, interoperability, regulatory compliance, and high costs. By examining case studies such as Estoniaâs healthcare system, IBM and Walmartâs Food Trust, and the Brooklyn Microgrid project, we explore the practical applications and benefits of combining big data with blockchain. Despite these hurdles, the review finds that the ongoing advancements and innovative solutions in these technologies offer significant promise. They are set to drive the adoption and effectiveness of decentralized decision-making, ultimately leading to better efficiency and outcomes across multiple sectors.
On blockchain platforms, an individual can use multiple wallet accounts to participate in transactions without disclosing identity. This poses great difficulty for entity behavior analytics on blockchain networks. To detect wallets belonging to the same owner, most solutions today rely on either off-chain data such as those on public forums and social media, or heuristic rules that pertain only to the specific blockchain network used. Their scalability and integrability are limited, especially upon changes in the underlying data structure or blockchain mechanisms. We propose to build a machine learning based solution that learns on on-chain transaction data, rather than relying only on heuristic rules. Specifically, we use heuristic methods to collect and label training data, and then apply our proposed machine learning technique to train the model. We focus on EVM blockchain networks and evaluated the proposed approach on two chains: Ethereum and BNB Chain, obtaining a dataset with over 3 million labeled wallet addresses. The detection accuracy can reach more than 90%. This is better than an existing commercial entity detection system which offers only 75%. Our prediction also is around two times better in terms of F-measure.
Ahmed Alagha, Hadi Otrok, Shakti Singh, Rabeb Mizouni ¡ 5 authors
Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its complexity, which requires expertise in training and designing DRL solutions, high computational capabilities, and sometimes access to pre-trained models. This necessitates the need for hassle-free services that increase the availability of DRL solutions to a variety of users. To enhance the accessibility to DRL services, this paper proposes a novel blockchain-based crowdsourced DRL as a Service (DRLaaS) framework. The framework provides DRL-related services to users, covering two types of tasks: DRL training and model sharing. Through crowdsourcing, users could benefit from the expertise and computational capabilities of workers to train DRL solutions. Model sharing could help users gain access to pre-trained models, shared by workers in return for incentives, which can help train new DRL solutions using methods in knowledge transfer. The DRLaaS framework is built on top of a Consortium Blockchain to enable traceable and autonomous execution. Smart Contracts are designed to manage worker and model allocation, which are stored using the InterPlanetary File System (IPFS) to ensure tamper-proof data distribution. The framework is tested on several DRL applications, proving its efficacy.
Koki Koshikawa, Jong-Deok Kim, WonâJoo Hwang, Kien Nguyen ¡ 5 authors
Blockchain holds significant potential in addressing the security, privacy, decentralization, and interoperability challenges prevalent in the Internet of Things (IoT), However, this advancement often comes at the expense of scalability. Therefore, enhancing the scalability of IoT blockchain systems while preserving other essential blockchain attributes is imperative. This paper aims to mitigate network latency in the blockchain network, a factor directly linked to blockchain scalability. Achieving this goal requires implementing an efficient peer selection method, moving beyond the reliance on default selection (i.e., the one in Bitcoin, Ethereum, etc.). In existing literature, Perigee has been introduced as a method that nearly optimizes the delay in the transaction transmission process. However, Perigee has not comprehensively addressed the complete transaction life cycle, which includes a crucial process-block transmission. In response to this limitation, we propose Dual Perigee, a solution that thoroughly considers and optimizes both transaction-oriented latency (TOL) and block-oriented latency (BOL). To show the effectiveness of Dual Perigee, we implemented and evaluated it within an emulated IoT-Blockchain system, comparing its performance with Perigee and the default peering method in Ethereum. The results reveal that Dual Perigee excels in reducing BOL compared to Perigee. Moreover, Dual Perigee exhibited a latency that was 43% and 80% lower than the default peering method and Perigee, respectively.
Cryptocurrencies are a revolution in the domain of economics, currency, and trade, Bitcoin being the prime among them. With the ever increasing demand and limited number of bitcoins available, bitcoin as well as other cryptocurrencies has been a hot topic among economists, traders, inverters, and researchers. Although these coins based on blockchain are phenomenal, the volatile nature of the cryptocurrency markets has spurred significant interest in developing accurate prediction models to aid investors and market participants. This research paper examines the application of the Bidirectional Long Short-Term Memory model for predicting Bitcoin prices. This study uses historical Bitcoin price data and features such as opening prices, closing prices, and trading volumes to predict the target closing prices for Bitcoins. The study evaluates the performance of the Bi-LSTM model against comparative models, considering various relevant metrics. It was observed that Bi-LSTM performs significantly better among other regression models in capturing the inherent volatility and non-linearity of cryptocurrency markets. Our proposed model outperformed previous studies in various reported metrics. Additionally, we explored the impact of various hyperparameters and input features on the modelâs performance to achieve an ideal state. Insights obtained by conducting this study would eventually contribute to an extensive understanding of the potential of deep learning techniques in forecasting cryptocurrency prices, offering valuable implications for investors and risk management strategies in the evolving landscape of digital assets.
In this paper, we undertake a thorough comparative examination of data resources pertinent to Non-Fungible Tokens (NFTs) within the framework of Machine Learning (ML). The core research question of the present work is how the integration of ML techniques and NFTs manifests across various domains. Our primary contribution lies in proposing a structured perspective for this analysis, encompassing a comprehensive array of criteria that collectively span the entire spectrum of NFT-related data. To demonstrate the application of the proposed perspective, we systematically survey a selection of indicative research works, drawing insights from diverse sources. By evaluating these data resources against established criteria, we aim to provide a nuanced understanding of their respective strengths, limitations, and potential applications within the intersection of NFTs and ML.
Bitcoin price volatility fascinates both researchers and investors, studying features that influence its movement. This paper expends on previous research and examines time series data of various exogenous and endogenous factors: Bitcoin, Ethereum, S&P 500, and VIX closing prices; exchange rates of the Euro and GPB to USD; and the number of Bitcoin-related tweets per day. A period of three years (from September 2019 to September 2022) is covered by the research dataset. A two-layer framework is introduced tasked with accurately forecasting Bitcoin price. In the first layer, to account for complexities in the analyzed data, variational mode decomposition (VMD) extracts trends from the time series. In the second layer, Long short-term memory and hybrid Bidirectional long short-term memory networks were used to forecast prices several steps ahead. This work also introduced an enhanced variant of the sine cosine algorithm to tune the control parameters of VMD and both neural networks for attaining the best possible performance. The main focus is on combining VMD with modified metaheuristics to improve cryptocurrency closing value forecast. Two sets of experiments were conducted, with and without VMD. The results have been contrasted with models tuned by seven other cutting-edge optimizers. Extensive experimental outcomes indicate that Bitcoin price can be forecasted with great accuracy using selected features and time series decomposition. Additionally, the best model was analyzed, and Shapley values indicated that features such as EUR/USD exchange rates, Ethereum closing prices, and GBP/USD exchange rates, have a significant impact on forecasts.
Abstract Cryptocurrencies have garnered significant attention recently due to widespread investments. Additionally, researchers have increasingly turned to social media, particularly in the context of financial markets, to harness its predictive capabilities. Investors rely on platforms like Twitter to analyze investments and detect trends, which can directly impact the future price movements of Bitcoin. Understanding and analyzing Twitter sentiments can potentially provide insights into future Bitcoin price movements and can shed light on how investor sentiment affects cryptocurrency markets. In this study, we explore the correlation between Twitter activity and Bitcoin prices by examining tweets related to Bitcoin price sentiments. Our proposed model consists of two distinct networks. The first network exclusively utilizes historical price data, which is further decomposed into various components using the Empirical Mode Decomposition method. This decomposition helps mitigate the impact of irregular fluctuations on Bitcoin price predictions. Each of these components is then separately processed by Long Short-Term Memory (LSTM) networks. The second network focuses on modeling user sentiments and emotions in conjunction with Bitcoin market data. User opinions are categorized into positive and negative classes and are integrated with historical data to predict the next-day price using LSTM networks. Finally, the outputs of each network are combined to form the ultimate prediction values. Experimental results demonstrate that Twitter sentiment can effectively helps us predict Bitcoin price trends. Furthermore, to validate our proposed model, we compared it with several state-of-the-art methods. The results indicate that our approach outperforms these existing models in terms of accuracy.
An address in the Bitcoin blockchain serves as an identifier for spending cryptocurrency. The blockchain itself does not contain information about the actual users who control the assets. Users have the ability to create multiple addresses, leading to the challenge of grouping addresses, also known as clustering, in order to analyze Bitcoin users. The grouping problem is a crucial initial step in the analysis of Bitcoin users. The grouping solution involves using heuristics based on usage patterns, with a focus on Common Spending (CS) and One-Time Change (OTC) in the current research. Additionally, anonymization techniques such as Shared Send Mixers (SSM) are considered in this paper as they prevent or at least complicate analysis. It is possible to untangle SSM, and based on the number of untanglings per transaction and their size, the transaction can be classified into a certain complexity class. Both heuristics and untangling were applied to the Bitcoin transaction history in our study. Our findings revealed that OTC misuse may occur in 19 to 26 percent of cases, depending on the specific algorithm used. Furthermore, CS and OTC were found to generate 13 to 0.8 percent of new cases when applied to subtransactions of separable SSM. Additionally, we demonstrated that SSM address grouping respects untangling complexity classes. In the future, we plan to adapt our workflow to other Bitcoin-like blockchains and modify our untangling algorithm to cover more SSM transactions.
Price Oracle Manipulation Attacks (POMAs) are increasingly occurring in blockchain systems, and result in significant financial loss. Prior work on detecting POMAs only considers single-transaction attacks, in which the entire attack is contained within a single transaction. We systematically study POMAs in blockchain systems (Ethereum). We find that POMAs that span multiple transactions have become much more frequent than single-transaction POMAs. Thus, there is a compelling need for a framework that can detect POMAs spanning multiple transactions. Moreover, there is a need to come up with generic rules for detecting POMAs rather than rely on past attack patterns like prior work has done.We first devise first-principle rules for detecting POMAs based on traditional stock market manipulation attacks. We then propose POMABuster, which leverages these rules to detect POMAs spanning both single and multiple transactions. POMABuster leverages common characteristics of POMA attackersâ behavior to optimize its detection. We evaluate POMABuster on 2.5 yearsâ worth of transactions from the blockchain, as well as a dataset compiled from the Code4rena audit reports. Our results demonstrate that POMABuster detects nearly 6.5X more POMAs than prior work. Further, POMABuster has a 1% worst-case false positive rate, and zero false negative rate, both of which significantly outperform prior work.
Hospitals and health systems still have numerous difficulties in setting up, maintaining, and modernizing their electronic health record systems today. The numerous problems with using EHRs are covered in this study along with potential fixes. Maintaining a single version of the truth and safely storing medical records are the goals. Using blockchain is a likely solution. A patient's data may be accessed by various organizations, including hospitals, clinics, labs, and other health insurers, in order to document transactions and fulfill their mandates on the distributed ledger. By leveraging the blockchain to provide a distributed access and validation system, a platform for safely storing and exchanging electronic health records can be developed, helping to fully replace the present centralized middlemen. Consequently, the issues with today's health records are resolved. KeywordsâElectronic Health Record, Distributed ledger, Blockchain, IPFS, BigchainDB.
The widespread adoption and success of blockchain, particularly Bitcoin, was influenced by the promise of decentralization and anonymity. Sadly, these same characteristics have made it attractive to illegal activities, requiring careful oversight and targeted interventions. In order to mitigate illicit usage of this technology, we need to analyze and de-anonymize transactions occurring in the blockchain. For that purpose, change addresses identification is a promising technique, since change addresses can be associated to the inputs of the same transaction since they are meant to hold leftover funds for the same user. In this article, we propose a new approach of change address detection using hierarchical clustering. First, we developed a new method for data extraction of connected transactions. After collecting the transaction, we combined multiple input heuristics with a hierarchical clustering algorithm at the transaction level to study similarities in usage patterns between inputs and outputs. After applying our detection model, we analyze the generating cluster and evaluate the performance of our solution in terms of F1-score, result accuracy, recall and precision.
In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts.
Blockchain technology has rapidly emerged to mainstream attention, while its publicly accessible, heterogeneous, massive-volume, and temporal data are reminiscent of the complex dynamics encountered during the last decade of big data. Unlike any prior data source, blockchain datasets encompass multiple layers of interactions across real-world entities, e.g., human users, autonomous programs, and smart contracts. Furthermore, blockchain's integration with cryptocurrencies has introduced financial aspects of unprecedented scale and complexity such as decentralized finance, stablecoins, non-fungible tokens, and central bank digital currencies. These unique characteristics present both opportunities and challenges for machine learning on blockchain data. On one hand, we examine the state-of-the-art solutions, applications, and future directions associated with leveraging machine learning for blockchain data analysis critical for the improvement of blockchain technology such as e-crime detection and trends prediction. On the other hand, we shed light on the pivotal role of blockchain by providing vast datasets and tools that can catalyze the growth of the evolving machine learning ecosystem. This paper serves as a comprehensive resource for researchers, practitioners, and policymakers, offering a roadmap for navigating this dynamic and transformative field.
In todayâs world almost all hospital uses hardcopy for patient data store and for booking appointment. All patient data is available on paper and user need to manage that all over he goes. Consider âAâ patient got admitted in City Pune, all his data is stored on paper and what medicines he took. All the info about his health is stored there. After someday âAâ patient gone to another city far away without carrying any documents and there he got and health emergency and is not in condition of speaking and need urgently help. But due to lack of info available about patient doctor could not urgently take decisions. So, to reduce this risk our system is developed. The distribution of Health records becomes a time consuming and expensive process when we use the traditional client-server healthcare data management system where each hospital/clinic maintains its own database of patientsâ medical records. A patientâs treatment is further delayed if the patient moves from one hospital to another hospital across different regions or countries. Moreover, most of the time a patient must repeat several laboratory and radiology tests. So, to address this the patientâs medical data from different hospitals are stored in a Blockchain based storage making it easily accessible by patients and the hospitals. And the pharma company will be able to store the information of medicines and medicals will be available to verify the medicines if they are from trustworthy companies. Keyword - - Dapp, Web3.Js, CSS.
T. Niedermayer, Pietro Saggese, Bernhard Haslhofer
The integration of bots in Distributed Ledger Technologies (DLTs) fosters efficiency and automation. However, their use is also associated with predatory trading and market manipulation, and can pose threats to system integrity. It is therefore essential to understand the extent of bot deployment in DLTs; despite this, current detection systems are predominantly rule-based and lack flexibility. In this study, we present a novel approach that utilizes machine learning for the detection of financial bots on the Ethereum platform. First, we systematize existing scientific literature and collect anecdotal evidence to establish a taxonomy for financial bots, comprising 7 categories and 24 subcategories. Next, we create a ground-truth dataset consisting of 133 human and 137 bot addresses. Third, we employ both unsupervised and supervised machine learning algorithms to detect bots deployed on Ethereum. The highest-performing clustering algorithm is a Gaussian Mixture Model with an average cluster purity of 82.6%, while the highest-performing model for binary classification is a Random Forest with an accuracy of 83%. Our machine learning-based detection mechanism contributes to understanding the Ethereum ecosystem dynamics by providing additional insights into the current bot landscape.
Crypto markets present significant challenges in financial time series forecasting with their high volatility and unpredictable nature. In this study, Optuna Based Optimized Transformer (OBOT) was proposed for time series forecasting for Bitcoin, the pioneer of cryptocurrency markets. To compare the proposed OBOT, Autoregressive Integrated Moving Average (ARIMA), Gradient Boosting Trees, Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Temporal Convolutional Network (TCN) with optimized hyperparameters were used. In particular, after the success of Transformer models in natural language processing, studies have been conducted on their potential for time series problems. The models were evaluated using Optuna for hyperparameter optimization and their performance was compared with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The generalized performance of the models was tested by dividing the data set into different time steps (6â12) and training and test sets at different rates (0.5-0.5, 0.7-0.3, 0.8-0.2). The results show that the proposed OBOT approach stands out for Bitcoin with an RMSE value of 0.0079 and a MAE value of 0.0122. These findings reveal that the proposed OBOT approach have significant potential in crypto market forecasting and should be examined in more detail in future studies.
Blockchain technology is extensively employed across various industries, transforming conventional processes, and enhancing efficiency, security, and transparency. The decentralized nature of blockchain prevents any single entity from having control, fostering trust, and minimizing the risk of manipulation. Proposing a blockchain solution is not enough as the success and adoption of such solutions heavily depends on the overall performance. The performance of the blockchain based solutions can be measured using various metrics such as latency, throughput, and CPU utilization. This study is using throughput as a performance metric and demonstrating how the Hyperledger Caliper tool can be used to measure it. To illustrate the use of Hyperledger Caliper, a scenario has been devised involving the presence of multiple organizations and peers within the blockchain network. This test-network comprises three organizations, three certificate authorities, three orderer-nodes and three peers per organization. The proposed network is set up using Hyperledger Fabric blockchain framework. This scenario depicts how remote patient monitoring systems work in a multi-organization environment. The experiment results observe a very minute difference in send rate and throughput when data is injected at 80 and 100 TPS during the write operations but this difference is missing during the read operations as send rate and throughput is observed almost identical.