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.
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.
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.
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.
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
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.
Mohammed Almukaynizi, Vivin Paliath, Malay Shah, Malav Shah · 5 authors
With the recent prevalence of darkweb/deepweb (D2web) sites specializing in the trade of exploit kits and malware, malicious actors have easy-access to a wide-range of tools that can empower their offensive capability. In this study, we apply concepts from causal reasoning, itemset mining, and logic programming on historical cryptocurrency-related cyber incidents with intelligence collected from over 400 D2web hacker forums. Our goal was to find indicators of cyber threats targeting cryptocurrency traders and exchange platforms from hacker activity. Our approach found interesting activities that, when observed together in the D2web, subsequent cryptocurrency-related incidents are at least twice as likely to occur than they would if no activity was observed. We also present an algorithmic extension to a previously-introduced algorithm called APT-Extract that allows to model new semantic structures that are specific to our application.
In this paper we describe the various scoring systems used to calculate rewards of participants in Bitcoin pooled mining, explain the problems each were designed to solve and analyze their respective advantages and disadvantages.