Stablecoins are becoming more common in the Fin-Tech (Financial Technology) ecosystem as they keep their value stable and combines easily with decentralized finance inherent in the financial technology ecosystem due to their price stability and simplicity of integration in decentralized finance (DeFi), cross-border payments, and automated trading systems. However, the same characteristics that propel utility transaction speed, pseudonymity, and automation through smart contracts have also made them vulnerable to financial manipulation. Tactics such as wash trading, spoofing, and pump-and-dump schemes have become more prevalent, compromising market integrity significantly. However, major technical challenge in detecting these fraudulent activities and behaviors, especially under conditions of extreme class imbalance even the legitimate transactions vastly outnumber fraudulent ones.This paper introduces SC-GAN, a conditional Generative Adversarial Network that addresses the scarcity of fraudulent samples by synthesizing realistic blockchain-based fraud instances. The model conditions on key financial and transactional features native to blockchain systems, enabling the generation of high-fidelity synthetic data. We then compare SC-GAN with traditional oversampling techniques like SMOTE and Borderline-SMOTE on a variety of supervised classification models. Our experiments on a real-world stablecoin transaction dataset show with the help of SC-GAN improves both the F1 Score and overall accuracy that is resulting in more efficient detection of rare but crucial fraudulent transactions. This approach also provides the possibility for stronger fraud prevention methods and risk management policies within FinTech platforms.
Transaction flow networks are crucial in detecting illicit activities such as wash trading, credit card fraud, cashback arbitrage fraud, and money laundering. \revise{Our collaborator, Grab, a leader in digital payments in Southeast Asia, faces increasingly sophisticated fraud patterns in its transaction flow networks. In industry settings such as Grab's fraud detection pipeline, identifying fraudulent activities heavily relies on detecting dense flows within transaction networks. Motivated by this practical foundation,} we propose the \emph{\(S\)-\(T\) densest flow} (\SDMF{}) query. Given a transaction flow network \( G \), a source set \( \Src \), a sink set \( \Dst \), and a size threshold \( k \), the query outputs subsets \( \Src' \subseteq \Src \) and \( \Dst' \subseteq \Dst \) such that the maximum flow from \( \Src' \) to \( \Dst' \) is densest, with \(|\Src' \cup \Dst'| \geq k\). Recognizing the NP-hardness of the \SDMF{} query, we develop an efficient divide-and-conquer algorithm, CONAN. \revise{Driven by industry needs for scalable and efficient solutions}, we introduce an approximate flow-peeling algorithm to optimize the performance of CONAN, enhancing its efficiency in processing large transaction networks. \revise{Our approach has been integrated into Grab's fraud detection scenario, resulting in significant improvements in identifying fraudulent activities.} Experiments show that CONAN outperforms baseline methods by up to three orders of magnitude in runtime and more effectively identifies the densest flows. We showcase CONAN's applications in fraud detection on transaction flow networks from our industry partner, Grab, and on non-fungible tokens (NFTs).
Este trabalho apresenta uma metodologia de detecção de contratos inteligentes do tipo mixers na rede Ethereum. Utilizou-se um modelo de aprendizado de máquina baseado em Random Forest, treinado com transações do Tornado Cash e balanceado com amostras de 100 endereços aleatórios não relacionados a mixers. O modelo foi treinado com dados de março de 2025 e validado em 29/10/2020, dia de alto volume de transações, identificando corretamente 3 endereços do Tornado Cash.
Quentin Botha, Laurent Bindschaedler, Christoph Siebenbrunner
Decentralized mathematics prediction markets promise new forms of collaboration and incentive alignment, but traditional requirements engineering methods fail to address the unique governance, incentive, and security challenges of such Web3 systems. This paper demonstrates how they can be addressed through a requirements-driven design of a decentralized prediction market for mathematical conjectures, and proposes concrete enhancements to existing frameworks. Our work delivers actionable guidelines for engineering secure, incentive-aligned decentralized platforms, and sets a new standard for early-stage RE in the Web3 era.
This study employs clustering analysis to evaluate the efficiency of GPUs used in cryptocurrency mining, categorizing them into distinct groups based on computational output and power consumption. Using K-Means clustering, GPUs were grouped into three clusters: low-efficiency, moderate-efficiency, and high-efficiency. High-efficiency GPUs demonstrated superior hash rates (e.g., 104.79 Mh/s for AbelHash and 218.35 Mh/s for Autolykos2) despite higher power consumption, making them ideal for high-performance mining operations. Conversely, low-efficiency GPUs exhibited lower computational output and modest energy use, highlighting opportunities for hardware upgrades or repurposing. Visualization techniques, including scatter plots and pair plots, provided clear distinctions between clusters, while a silhouette score of 0.35 indicated moderate cluster separation, suggesting areas for further refinement. The findings offer actionable insights for optimizing hardware selection, reducing operational costs, and improving energy efficiency in mining operations. Additionally, this study underscores the importance of sustainability in cryptocurrency mining and provides a foundation for future research, including the integration of additional performance metrics, exploration of alternative clustering algorithms, and development of energy-efficient mining practices. These insights contribute to the broader goal of fostering a more sustainable and data-driven approach to cryptocurrency mining.
Abstract Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A slight improvement in forecasting performance can lead to increased profitability; Therefore, obtaining a realistic forecast is very important for investors. Bitcoin, frequently mentioned in recent due to its volatility and chaotic behavior, has become an investment tool, especially during and after the COVID-19 pandemic. In this study, selected ML techniques were investigated for predicting cryptocurrency movements by using technical indicator-based data sets and measuring the applicability of the techniques to cryptocurrencies that do not have sufficient historical data. In order to measure the effect of data size, Bitcoin’s last 1 year and 7 years of data were used. Following the related literature, Google trends and the number of tweets were used as input features, in addition to the most commonly used twelve technical indicators. Random Forest, K-Nearest Neighbors, Extreme Gradient Boosting (XGBoost-XGB), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) network were optimized for best results. Accuracy, F1, and area under the ROC curve values were used to compare the model performance. For continuous data, ANN and SVM performed the best with the highest accuracy and outperformed the other ML models for complete and reduced sets. LSTM reached the best accuracy for trend data, but SVM, NB, and XGB models showed similar performance. The research shows that some indicators significantly affect prediction performance, and the data discretization process also improved the model’s accuracy. While the number of samples affects the results of many ML models, correctly optimized and fine-tuned models may also give excellent results even with less data.
We analyze developer activity across 10 major Ethereum repositories (totaling 129884 commits, 40550 issues) spanning 10 years to examine how events such as technical upgrades, market events, and community decisions impact development. Through statistical, survival, and network analyses, we find that technical events prompt increased activity before the event, followed by reduced commit rates afterwards, whereas market events lead to more reactive development. Core infrastructure repositories like Go-Ethereum exhibit faster issue resolution compared to developer tools, and technical events enhance core team collaboration. Our findings show how different types of events shape development dynamics, offering insights for project managers and developers in maintaining development momentum through major transitions. This work contributes to understanding the resilience of development communities and their adaptation to ecosystem changes.
The environmental impact of Bitcoin mining in Kazakhstan, which is currently the third-largest market in the world by hash rate, is coming under further scrutiny. Data on the production of renewable energy and related carbon footprints are essential for evaluating the situation. To create a thorough picture of how Bitcoin mining and environmental responsibility connect in Kazakhstan, this paper allows for the analysis and prediction of the interactions between carbon emissions, renewable energy use, and Bitcoin mining. Using a q-rung orthopair fuzzy hypersoft set (q-ROFHS)-based multi-criteria decision-making technique can improve research on the environmental effects of Bitcoin mining, the integration of renewable energy sources, and the corresponding carbon footprints. The analytic hierarchy process is used to identify the best pollution reduction strategies while taking feasibility and cost-effectiveness into account. The proposed approach will assist the business in achieving its environmental objectives, lessen its negative effects on the environment, and promote a greener future. This study guarantees a more precise and dependable evaluation of pollution control tactics, considering not only the effects on the environment but also practicality and affordability. The outcomes highlight the developed approach's effectiveness and stability in managing complicated information within the parameters of q-ROFHS.
S. Kiruthiga, R. Balamanigandan, R Mahaveerakannan, A. Mary Jenifer
The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P<0.05) there is a statistical significance (p=0.007) between these two algorithms. The Novel RF Algorithm achieved an accuracy of 87.7330%, while the KNN Algorithm achieved an accuracy of 72.1250%. When the two algorithms were compared using an independent sample t-test, the difference in accuracy was found to be statistically significant at 0.760.
Data lineage technology is a method of describing the relationships between data, which plays a crucial role in solving many challenges in the data marketplace, such as unauthorized data redistribution, data tampering, and false data transactions. However, the complexity of the multi-chain data marketplace, including factors such as data credibility, trust environment, data ownership, and relationship complexity, poses significant challenges to construct data lineages. To effectively address these challenges, we propose a data lineage construction method for multi-chain data marketplaces. This method mainly involves the following three steps: Firstly, map data assets to Data Non-Fungible Token (DataNFT) and use referable NFT (rNFT) to record data lineage. Secondly, when data assets require cross-chain transfer, the transfer message of DataNFT is broadcasted through the interchain NFT protocol, without the need for actual cross-chain transfer. Finally, we add weights to the data lineage link, enabling us to quickly locate problematic data based on weight sorting during data auditing. We have implemented a system prototype of this method and verified its effectiveness through experiments. The experimental results show that our proposed scheme not only ensures the correctness and completeness of data lineages, but also effectively reduces audit costs.
Mohammed Alghazwi, Dewi Davies-Batista, Dimka Karastoyanova, Fatih Türkmen
Aggregate statistics play an important role in extracting meaningful insights from distributed data while preserving privacy. A growing number of application domains, such as healthcare, utilize these statistics in advancing research and improving patient care. In this work, we explore the challenge of input validation and public verifiability within privacy-preserving aggregation protocols. We address the scenario in which a party receives data from multiple sources and must verify the validity of the input and correctness of the computations over this data to third parties, such as auditors, while ensuring input data privacy. To achieve this, we propose the "VPAS" protocol, which satisfies these requirements. Our protocol utilizes homomorphic encryption for data privacy, and employs Zero-Knowledge Proofs (ZKP) and a blockchain system for input validation and public verifiability. We constructed VPAS by extending existing verifiable encryption schemes into secure protocols that enable N clients to encrypt, aggregate, and subsequently release the final result to a collector in a verifiable manner. We implemented and experimentally evaluated VPAS with regard to encryption costs, proof generation, and verification. The findings indicate that the overhead associated with verifiability in our protocol is 10x lower than that incurred by simply using conventional zkSNARKs. This enhanced efficiency makes it feasible to apply input validation with public verifiability across a wider range of applications or use cases that can tolerate moderate computational overhead associated with proof generation.
The widespread availability of tools to collect and share spatial data enables us to produce a large amount of geographic information on a daily basis. This enormous production of spatial data requires scalable data management systems. Geospatial architectures have changed from clusters to cloud architectures and more parallel and distributed processing platforms to be able to tackle these challenges. Peer-to-peer (P2P) systems as a backbone of distributed systems have been established in several application areas such as web3, blockchains, and crypto-currencies. Unlike centralized systems, data storage in P2P networks is distributed across network nodes, providing scalability and no single point of failure. However, managing and processing queries on these networks has always been challenging. In this work, we propose a spatio-temporal indexing data structure, DSTree. DSTree does not require additional Distributed Hash Trees (DHTs) to perform multi-dimensional range queries. Inserting a piece of new geographic information updates only a portion of the tree structure and does not impact the entire graph of the data. For example, for time-series data, such as storing sensor data, the DSTree performs around 40% faster in spatio-temporal queries for small and medium datasets. Despite the advantages of our proposed framework, challenges such as 20% slower insertion speed or semantic query capabilities remain. We conclude that more significant research effort from GIScience and related fields in developing decentralized applications is needed. The need for the standardization of different geographic information when sharing data on the IPFS network is one of the requirements.
Globally, increasing amount of cryptocurrency mining demand presents both opportunities and challenges for electric energy systems. This research employs a data-driven method to predict cryptocurrency mining load-tripping events, specifically targeting the low-voltage ride-through (LVRT) problem. The study utilizes diverse low-voltage fault scenarios generated through electromagnetic transient program (EMTP) software as training data. For fault classification, a convolutional neural network (CNN) is employed to improve model accuracy. Additionally, model explainability is enhanced using a decision tree for forecasting tripping events. The proposed approach is validated on a 6-bus power system integrated with cryptocurrency mining facilities.
탈중앙화된 자율조직은 블록체인 네트워크에 기반하여 활동하는 국제적인 단체로서 이사회와 같은 경영진이 아닌 구성원들이 직접 자동화된 의사결정시스템을 이용하여 의사결정을 내리고, 그 같은 단체의사에 따라 운영되는 인적 단체이다. 이들이 탈중앙화된 가버넌스를 구축하는 과정은 사단이 정관 기타 규약을 마련하고 그에 따라 조직을 갖춰서 독립된 사회적 실체로 인정받는 과정과 유사한 면이 있다. 따라서 탈중앙화된 자율조직은 사단의 성격을 가지고 있다고 볼 수 있다. 국제적인 단체로서의 성격을 띄는 탈중앙화된 자율조직의 단체법적 지위를 판단하기 위해서는 국제조약과 국제사법상의 쟁점을 고려해야 한다. 다만, 외국의 경우, 대체로 이들을 조합이나 파트너쉽과 유사한 단체로 보아 구성원의 유한책임을 부정하는 경향이 있다. 그리하여 그 구성원들에게 유한책임을 인정하고 탈중앙화된 자율조직의 독립된 사회적 실체로서의 지위를 인정하기 위해, 이들을 유한책임회사로 인정하려는 입법례가 생기기도 하였다. 이들 조직을 우리나라 단체법 관점에서 살펴보는 경우, 법인 아닌 사단으로 평가할 수 있을지를 살펴볼 필요가 있다. 왜냐하면, 이들이 법인 아닌 사단으로 취급되는 경우, 총유규정에 따라 구성원의 책임재산이 분리되어 사실상 유한책임과 같은 효과를 얻을 수 있기 때문이다. 다만, 문제는 탈중앙화된 자율조직이 가지고 있는 특징들을 우리 민법상의 법인 아닌 사단의 법리로 포섭할 수 있는지 여부이다. 예를 들면, 대표자가 선임되어 있지 않고, 구성원지위 득실 변경을 가버넌스 토큰이라는 가상자산과 연동시키고, 조직이 관리하는 가상자산을 구성원들에게 분배하고, 영리 목적으로 운영되더라도 이를 민법상 법인 아닌 사단으로 취급할 수 있을지를 살펴보아야 한다. 계속해서 온라인을 기반으로 하여 다양한 국제적 성격을 띈 단체가 생겨날 것이다. 이들이 법인격을 취득하지 못한 경우, 그 속인법을 어떻게 결정하고, 국내 단체법상 이들을 어떻게 평가해야 할지가 고민될 수 밖에 없다. 우리 법인 아닌 사단 제도에 대한 비판이 있기는 하지만, 민법 개정 작업이 마무리되기 전까지는 이 같은 단체의 국내 단체법상 지위를 살펴볼 때 우리나라 특유의 법인 아닌 사단으로 취급할 수 있을지 그리고 그 실익이 무엇일지도 고민해 볼 필요가 있다. A Decentralized Autonomous Organization (DAO) is an international organization that operates based on a blockchain network. It is a member-managed association that its members make decisions using an automated decision-making system by themselves without the board of directors and is operated according to the groups decision. The process of establishing decentralized governance is similar to the process in which an association is recognized as an independent social entity by establishing its organs according to its articles and bylaws. Therefore, a Decentralized Autonomous Organization can be seen as having the characteristics of an association. The governing law of DAO shall be determined by international treaties and Conflict of Laws. Foreign countries seem to have a tendency to deny DAO members' limited liability by viewing DAO as an entity similar to partnership. In order to allow limited liability to its members, some jurisdictions made new law to recognize DAO as LLC. When examinging DAO from Korean entity law perspective, it is necessary to consider whether they can be evaluated as an unincorporated association. If DAOs can be treated as an unincorporated association, DAOs creditor can not be reimbursed from members asset, the same effect as limited liability. However, the question is whether characteristics of DAO are permissible under the legal principles of traditional unincorporated association. For example, it is also necessary to consider whether its activities would fall under the legal frame of unincorporated association laws in Korea as DAO has no representative, distribute virtual asset to its members, and even can be operated for profit. There will continue to emerge various online based international entities. If they are not incorporated in any jurisdiction, it is necessary evaluate their legal status from the Korean unincorporated entity law perspective based on Conflicts of Law approach, When evaluating their legal status, it is necessary to consider whether they can be treated as Koean unincorporated association and pros and cons as well.
Lucas Penteado Lopes da Silva, Luiz Adeildo da Silva, Josafat Marinho Falcão Neto, Geidson Benício Coelho de Souza
Elaboração de algoritmos de aprendizado de máquina para a previsão do comportamento de preços da criptomoeda Ethereum, utilizando-se uma base de dados pública (Kaggle). Os modelos elaborados foram do tipo linear (ARIMA, séries temporais) e nãolinear (três modelos de redes neurais LTSM). Como melhor resultado, verificou-se que um dos modelos não lineares foi capaz de realizar previsões distantes em média de 4,32% dos preços reais.
José Benito Hernández C., Andrés García-Medina, Miguel Andrés Porro V.
We studied the effects of the recent financial turbulence of 2020 on the cryptocurrency market, taking into account both prices and volumes from December 2019 to July 2020. Time series were transformed into transaction matrices, and the Apriori algorithm was applied to find the association rules between different currencies, identifying whether the price or the volume of the currencies compose the rules. We divided the data set into two subsets and found that before the decline in cryptocurrency prices, the association rules were generally formed by these prices and that, then, the volumes of the transactions dominated to form the association rules.
Anna Chukhnina, Grigorii Melnikov, Anton Pecherkin, Александр Владимирович Соколов · 5 authors
Many research papers with data processing are released daily. Some of them are prone to statistical errors caused by selective reporting. It results in a lack of reproducibility and wrong conclusions. In the Non-selective reporting demo, we show how to deal with the reporting bias problem through a distributed ledger system.
Homomorphic Encryption and Zero Knowledge Proofs are two trending concepts that are widely popular as data privacy preservation techniques in a wide variety of applications, especially in those associated with the newly evolved block chain technology which are immutable, distributed and secure. Zero knowledge proof is a cryptographic technique can provide proof that a certain statement is correct, without revealing any details about the statement, while homomorphic encryption allows to perform computations on encrypted data without decrypting it. This article explores the significance of the data privacy aspect provided by both ZKP and Homomorphic Encryption and how it can be effectively used to improvise the privacy of blockchain applications in various domains.\n\n
Leakage of protected data became an acute topic. The system tracking who, where and when has accessed a certain record of such a type of data could be a help in investigating vulnerabilities and weaknesses in defense. Also, it could name and point out responsible staff for the leakage with all legal and justice consequences. The paper considers an approach to build a system to register such kind of facts. The essence is to apply the distributed ledger technology, which is an open data storage. The system allows you to identify users who are trying to retrieve valuable information. At present, a technical and theoretical basis is ready for such solutions. Analysis of the current situation in the area under consideration shows that all the leading players in this segment of the IT market, in parallel with the development of mathematical models and methods of problem-oriented data mining, pay significant attention to the development of special software and hardware tools to support the performance of such tool solutions.
Open access
Data Mining Algorithms and Applications
Advanced Research in Systems and Signal Processing
Mahdi Alkaeed, Zaid Alamro, Muhammed Al-Ali, Hasan Abbas Al-Mohammed · 5 authors
Nowadays cryptography and their technologies have been with us for a long time. This field of science is developing day by day. Blockchain has come a long way since the birth of its first application. This application is Bitcoin which depends on Blockchain or DLT (Distributed Ledger Technology). Bitcoin is the most famous cryptocurrency intended for mass which appeared the first time in 2008. This innovation led to sparked in the digital revolution, which provides for many sectors and industries the security features, decentralization, and a reliable mechanism for transmission and storing data. This new technology led to the meteoric rise of Bitcoin and other such cryptocurrencies, so this has aroused the interest of governments and emerging companies to get an effective role in competition. We can apply blockchain in different sectors and different Internet of things (IoT). Cryptocurrency mining has become a major concern for computer professionals and also for those who earn more money from an additional source. Mining is a process for settle mathematical issues that depend on the strength and speed of the devices to solve those problems, which by resolving them leads to getting rewards in the form of a digital currency. In this paper, we will highlight the benefits of using both CPU (Central Processing Unit) and GPU (Graphical Processing Unit) and a practical comparison between them to find out the best and fastest based on previous studies and on the characteristics and architecture of each.
Clément Bénard, Gérard Biau, Sébastien da Veiga, Erwan Scornet
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules. State-of-the-art learning algorithms are often referred to as "black boxes" because of the high number of operations involved in their prediction process. Despite their powerful predictivity, this lack of interpretability may be highly restrictive for applications with critical decisions at stake. On the other hand, algorithms with a simple structure-typically decision trees, rule algorithms, or sparse linear models-are well known for their instability. This undesirable feature makes the conclusions of the data analysis unreliable and turns out to be a strong operational limitation. This motivates the design of SIRUS, which combines a simple structure with a remarkable stable behavior when data is perturbed. The algorithm is based on random forests, the predictive accuracy of which is preserved. We demonstrate the efficiency of the method both empirically (through experiments) and theoretically (with the proof of its asymptotic stability). Our R/C++ software implementation sirus is available from CRAN.
Due to the anonymity of cyrptocurrencies, a new method of money laundering that hides the source of illegal funds has emerged by utilizing cyrptocurrencies. In addition, there are cases such as exchange hacks or ICO frauds in which cryptocurrencies themselves are dirty money. These illegal actions are criticized as problems of cryptocurrencies, and these problems must be tackled for the sake of development and commercialization of cryptocurrencies. Although Ethereum is a public blockchain that is completely open anyone, it does not provide the necessary tools such as query language or client tools needed, therefore requiring further methods to process and utilize data efficiently. This paper aims to explore the money laundering that is happening in Ethereum. In addition, it suggests the ways to sort Ethereum accounts suspected of being used for money laundering and the strategy to implement them using Graph Database.