Blockchain Papers

Xiong Huanliang, Wu Canghai, Jiaxin Chen, Yinglong Wang · 5 authors

No abstract is available for this record.

Paulin Kamuangu

Abstract: The Financial Technology (Fintech) sector is changing at a swift pace, as artificial intelligence (AI) is extending its influence. Greater complexity and global linkages are going to demand from fintech the power to rethink the integrity of its cybersecurity mechanisms and fraud tactics that have gotten intense up to a growing extent. The paper argues for the necessity of an "Algorithmic Fortress," an AI-driven cybernetic system incorporating all possible technologies targeted at securing digital financial networks against cyber-attacks and acts of financial fraud. The article delves into AI/ML, deep learning, anomaly detection through generative adversarial networks, etc., scope to predict battle, detect and fight problems. It does address adverse effects of AI risk, threatened system independence through synthetic identity fraud, application of AI for fraud detection in decentralized finance, DeFi, as well as the threat-hunting models that need to become autonomous. Supervised learning, unsupervised learning, and reinforcement learning are examination methodologies that are being applied in taking high recourse to the preservation of cybersecurity amongst their uncertainties. Our analysis will involve different experimentations of Python-based simulated attack scenarios to compare the two forms of cybersecurity. Also brought in are SmartArt visual representations revealed in multi-tier defensive architectures, combined with some strategic recommendations destined to protect future-facing fintech infrastructures from doing illicit deeds of algorithms. This study sketches possible solutions for securing the future-ready, trustworthy, and resilient fintech ecosystems once assisted by AI-enhanced, digital fortresses.

Sankul, Saira Gori

Money laundering has long been a major issue for governments, law enforcement agencies, and financial institutions around the globe. As technology advances, so too do money laundering methods, presenting new challenges for authorities and financial entities. Organised Crime Groups (OCGs) are increasingly exploiting digital platforms, cryptocurrencies, and virtual assets to disguise illicit funds while maintaining anonymity and complicating their transactions. This article analyses the problem of cryptocurrency laundering by the OCGs and various tactics employed by the OCGs to cover their trails. This article also in-depth discusses the international instruments such as the United Nations Convention against Transnational Organised Crime and Financial Action Task Force recommendations on the prevention of cryptocurrency laundering. The special focus of this paper is on the legal framework regarding cryptocurrency laundering in the United States, European Union and Malaysia. The findings of the paper suggest that there is a regulatory framework present in these jurisdictions but their regulations are not subject specific and regulatory powers have been granted to the authorities that are not specialised and skilled to tackle the problem of combating cryptocurrency laundering by OCGs.

Bowen He, Xiaohui Hu, Yufeng Hu, Ting Yu · 7 authors

The prosperity of Ethereum has led to a rise in phishing scams. Initially, scammers lured users into transferring or granting tokens to Externally Owned Accounts (EOAs). Now, they have shifted to deploying phishing contracts to deceive users. Specifically, scammers trick victims into either directly transferring tokens to phishing contracts or granting these contracts control over their tokens. Our research reveals that phishing contracts have resulted in significant financial losses for users. While several studies have explored cybercrime on Ethereum, to the best of our knowledge, the understanding of phishing contracts is still limited. In this paper, we present the first empirical study of phishing contracts on Ethereum. We first build a sample dataset including 790 reported phishing contracts, based on which we uncover the key features of phishing contracts. Then, we propose to collect phishing contracts by identifying suspicious functions from the bytecode and simulating transactions. With this method, we have built the first large-scale phishing contract dataset on Ethereum, comprising 37,654 phishing contracts deployed between December 29, 2022 and January 1, 2025. Based on the above dataset, we collect phishing transactions and then conduct the measurement from the perspectives of victim accounts, phishing contracts, and deployer accounts. Alarmingly, these phishing contracts have launched 211,319 phishing transactions, leading to 190.7 million in losses for 171,984 victim accounts. Moreover, we identify a large-scale phishing group deploying 85.7% of all phishing contracts, and it remains active at present. Our work aims to serve as a valuable reference in combating phishing contracts and protecting users' assets.

Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Christos Klavdianos

The rapid growth of digital platforms has fundamentally reshaped network and viral marketing, profoundly transforming how information spreads across social networks and influences consumer behavior. This comprehensive review synthesizes theoretical, computational, and ethical perspectives into an integrated narrative, providing novel insights into the mechanisms driving information diffusion within contemporary interactive marketing. By integrating foundational concepts from social network theory, advanced graph models, and behavioral dynamics, the paper demonstrates how the interplay between network structures, influencer behaviors, and AI-driven algorithms significantly redefines traditional marketing paradigms. A distinctive theoretical contribution of this study lies in its innovative combination of Big Data analytics with AI-based predictive modeling, explicitly revealing how real-time algorithmic personalization not only enhances marketing effectiveness but also creates new ethical tensions surrounding misinformation, algorithmic bias, and consumer vulnerability. Addressing recent calls for greater theoretical originality and narrative coherence in interactive marketing research, this review explicitly highlights how these insights resolve critical theoretical puzzles and clarify contemporary ethical dilemmas. Additionally, the paper identifies emerging trends—including Web3 marketing, decentralized platforms, and neuroscience-driven targeting—offering clear future research directions. Through its integrative, narrative-driven framework, this study significantly advances interactive marketing theory, providing essential guidance for scholars and practitioners navigating the evolving complexities of digital influence.

Ramy El-Kady

The chapter delves into the digital forensics of cryptocurrencies, focusing on the role of blockchain, computers, and mobile phones in gathering evidence. It underscores the pressing need for immediate and extensive research in digital cryptocurrency forensics, especially for significant cryptocurrencies increasingly attracting legitimate and malicious users. The chapter also highlights a critical gap in research on host-based cryptocurrency forensics, particularly mobile-based forensics. Most studies on host-based forensics concentrate on outdated operating systems or platforms, highlighting the necessity for more current versions, and the urgency of updating your methods in host-based forensics is apparent. Cryptocurrency forensics primarily involves the analysis of publicly accessible blockchains using clustering heuristics and machine learning-based analysis to identify anonymous entities or provide investigation guidance. Security and vulnerability assessment studies are crucial in examining forensic methods for cryptocurrencies, offering insights into potential exploits.

J. Chen, Jieli Liu, Jianlin Wu, Dan Lin · 6 authors

In recent years, blockchain technology has developed rapidly and received widespread attention. However, its pseudonymous and decentralized nature has also attracted many criminal activities. Ponzi schemes, a kind of classic financial scam, also hide their true face in smart contracts, causing massive financial losses to blockchain users. Although several methods have been proposed to detect Ponzi contracts, there are still limitations in broad applicability, semantics understanding, and adversarial robustness. In this article, we propose PonziHunter, an intelligent framework for hunting Ponzi contracts on Ethereum. To tackle the problem of broad applicability, we train a detection model that does not require expert experience based on publicly available on-chain bytecode and off-chain contract labels. To tackle the problem of semantics understanding, we employ cross-function control flows and state variable dependencies to understand the logic of Ponzi contracts. Specifically, we decompile bytecodes into higher-order representations to analyze control flows and state variable dependencies and model the information as graph data. By combining the idea of code slicing, we identify the basic blocks related to Ponzi contract recognition. To tackle the problem of adversarial robustness, we model Ponzi contract recognition as a graph classification problem based on contrastive pre-training. We propose a data augmentation method for control flow graphs (CFGs), which preserves the basic blocks related to Ponzi contract recognition as much as possible during data perturbation. Experimental results show that PonziHunter outperforms state-of-the-art tools with average improvements of at least 4.77% on real-world ground-truth data and can newly discover 85 Ponzi contracts in the wild. More importantly, PonziHunter is robust against adversarial examples and can locate the critical basic blocks for smart Ponzi detection.

Chang Xu, Huaiyu Xu, Liehuang Zhu, Xiaodong Shen · 5 authors

As blockchain technology becomes prevalent, smart contracts have shown significant utility in finance and supply chain management. However, vulnerabilities in smart contracts pose serious threats to blockchain security, leading to substantial economic losses. Therefore, developing effective vulnerability detection solutions is urgent. To address this issue, we propose a method for detecting vulnerabilities in smart contracts using graph neural networks (GNNs) that can identify eight common vulnerabilities. Our method is fully automated, applicable to all Ethereum smart contracts, and does not require expert-defined rules or manually defined features. We extract the Control Flow Graph and Abstract Syntax Graph from the smart contract code, which are then processed by a GNN to generate feature vectors for classification. Experiments on a real Ethereum dataset demonstrate that our method significantly outperforms existing state-of-the-art approaches. For individual detection tasks, the combined source code and bytecode method achieves an average accuracy of 95.78%, with a peak of 99.13%, and an average F1 score of 93.80%. Compared to competitors, our method shows an average improvement of 51.92% in accuracy and 47.21% in F1 score. The bytecode-only method achieves an average accuracy of 94.68% and an F1 score of 92.36%. For multi-class tasks, both methods achieve high accuracies of 91.26% and 87.34%, with F1 scores of 97.42% and 96.43%, respectively.

Mohamed Fathi, Muhammad bin Saud Al-Shammar, Gamal Sayed Khalifa Mohamed

Purpose This study aims to contribute to comprehending the challenges faced by Saudi Arabia in tackling the financial crimes enabled by cryptocurrency, especially in decentralized environments. Design/methodology/approach Content analysis is used in this study to assess the effectiveness of legal and regulatory reforms implemented in Saudi Arabia. It analyzes existing literature, case studies and relevant legal frameworks. Findings Analyzing the Saudi judiciary system, the research shows that there is a lack of proper approaches for preventing cryptocurrency-related crimes. A lack of awareness among consumers and investors exacerbates these challenges. Practical implications The Saudi government needs to improve the current legal system against the financial crimes linked with cryptocurrency: this entails enhancing public education, enhancing police capacity, as well as enhancing cooperation between nations. Originality/value This paper fulfills an identified need for research on the legal challenges Saudi Arabia faces in addressing cryptocurrency-related financial crimes within the context of Islamic law and its regulatory framework.

Ramy El-Kady

No abstract is available for this record.

Punam Bedi, Vinita Jindal, Ningyao Ningshen, Pushkar Gole

Smart contracts represent a predefined set of rules invoked when specific conditions are met within blockchain networks, eliminating the need for centralized authority to validate transactions. The absence of central authority can potentially expose smart contracts to fraudulent behavior. Moreover, implementation flaws in smart contracts can be exploited to cause unintended behavior, resulting in security or financial risks. Traditionally, the identification of vulnerabilities in smart contracts has relied on methods such as pattern matching, data flow analysis, and input testing. While these techniques are foundational, they are constrained by human limitations and may not comprehensively address the full spectrum of potential issues. This necessitates more advanced approaches to ensure robust security and reliability. Therefore, in the literature, numerous researchers have leveraged different Machine Learning (ML) and Deep Learning (DL) techniques to classify normal and malicious smart contracts. However, existing literature either grapples with class imbalance issues or relies on conventional methods. Moreover, existing research often falls short of locating the exact location of malicious code within the smart contracts. Therefore, to address these gaps, this paper proposes a novel model called the Dual-Branch Encoder Siamese Network (DBESN) for detecting malicious smart contracts. Furthermore, this model is extended to precisely identify the region of the vulnerable code fragment within the smart contract using the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. Experimental results demonstrated a performance Accuracy of 98.62% and 99.30% F1-Score with an inference time of 0.296 seconds. Given the high performance coupled with the low inference time of the proposed DBESN model, it is suitable for deployment within blockchain networks to detect and identify malicious smart contracts effectively and efficiently.

Jingyu Huang, Xiaorui Gong, Xiu Zhang

Existing static detection methods often fail to cap-ture dynamic interactions in smart contracts, resulting in low detection accuracy. Noise from irrelevant data can also affect the precision of vulnerability detection. This paper introduces a new method for detecting malicious smart contracts-GLM-ABLSTM-A, which integrates a General Language Model (GLM) with an Attention-based Long Short-Term Memory (ABLSTM) network. The method aims to address the limitations of static detection techniques, such as low accuracy and limited practicality, focusing on the interactivity and collaboration of smart contract systems. It compiles malicious contract code into Java and labels it, then preprocesses the code with GLM to ex-tract relevant textual information, reducing noise in the detection process. Finally, the extracted feature vectors are fed into the ABLSTM-A classifier. This technique introduces a feature extraction framework based on GLM, combined with the ABLSTM-A classifier, which enhances both the accuracy and efficiency of malicious contract detection and improves the in-teractivity and adaptability of the detection system.

Peter Olusegun Aina

In an increasingly digitalized and hyperconnected financial landscape, the complexity and frequency of cyber threats have grown exponentially, exposing financial institutions to real-time risks that conventional defense mechanisms struggle to mitigate.Traditional security frameworks, often reactive and siloed, lack the speed and contextual awareness required to protect dynamic finance ecosystems driven by automated trading, open banking, and decentralized financial services.This paper explores the emerging paradigm of Integrative Analytics for Autonomous Threat Response (IAATR)-a strategic synthesis of artificial intelligence (AI), behavioral modeling, and real-time analytics to secure business processes within finance ecosystems.From a broad perspective, the integration of AI into cybersecurity presents transformative possibilities.Machine learning models trained on network telemetry, user behavior, and transaction anomalies can detect threats proactively, adapt to novel attack patterns, and initiate countermeasures with minimal human intervention.The paper discusses how autonomous systemsrooted in deep reinforcement learning and explainable AI-enhance threat triage, isolate compromised processes, and orchestrate secure workflow rerouting to minimize systemic disruption.Narrowing the focus to finance-specific applications, the paper examines use cases including algorithmic fraud detection, insider threat mitigation in payment systems, and AI-enabled compliance monitoring.Emphasis is placed on the design of feedback loops between security intelligence layers and business process management (BPM) engines, ensuring that threat responses remain aligned with regulatory standards and operational continuity.The study concludes with a discussion on governance, ethical risks, and the role of digital trust in advancing AI-secured business environments.IAATR represents not just a technological leap, but a foundational shift toward anticipatory, resilient financial security architectures.

Kotidis, Leonidas

Η παρούσα διπλωματική εργασία εξετάζει τις επιθέσεις μηδενικής μεταφοράς στην αλυσίδα μπλοκ Ethereum, οι οποίες είναι μια μορφή απάτης, όπου οι επιτιθέμενοι εξαπατούν τους χρήστες, πείθοντάς τους να στείλουν χρήματα σε λάθος πορτοφόλι, στέλνοντας ψευδείς συναλλαγές μηδενικής αξίας. Ενώ η γενική ιδέα αυτών των επιθέσεων είναι γνωστή, δεν είναι ευρέως διαδεδομένη και δεν έχει γίνει αρκετή εκτενής έρευνα για να αποδειχθεί πόσο συχνά συμβαίνουν ή πώς να ανιχνεύονται αποτελεσματικά. Για να διορθώσουμε αυτό το κενό, κατασκευάσαμε ένα πλαίσιο Python χρησιμοποιώντας το Selenium για τη συλλογή δεδομένων πορτοφολιών απευθείας από το Etherscan. Συλλέξαμε περισσότερα από 18.000 πραγματικά ιστορικά πορτοφολιών χρηστών και φιλτραρίσαμε τις διευθύνσεις που ανήκουν σε ανταλλακτήρια ή μη πορτοφόλια. Στη συνέχεια, αναλύσαμε κάθε συναλλαγή για να εξαγάγουμε πληροφορίες όπως διευθύνσεις πορτοφολιών, χρονικές σφραγίδες, ποσά και αναγνωριστικά συναλλαγών. Σχεδιάσαμε δύο εκδοχές μεθόδων ανίχνευσης, μία χαλαρή και μία αυστηρή. Η χαλαρή εκδοχή εντόπισε περισσότερες πιθανές επιθέσεις, αλλά είχε περισσότερους ψευδώς θετικούς. Η αυστηρή εκδοχή πρόσθεσε ελέγχους για τους πρώτους και τελευταίους τέσσερις χαρακτήρες της διεύθυνσης του πορτοφολιού και σήμανε μόνο συναλλαγές κάτω από 2 δολάρια για να βελτιώσει την ακρίβεια. Στην αυστηρή εκδοχή, συνολικά, το 3,96% των πορτοφολιών είχαν γίνει στόχος, και ανιχνεύτηκαν περισσότερες από 11.600 επιθέσεις. Από αυτές, οι 1.205 ήταν επιτυχείς, αν και οι περισσότερες οδήγησαν μόνο σε μικρές απώλειες. Τα αποτελέσματα δείχνουν ότι, ενώ αυτές οι επιθέσεις δεν αποφέρουν μεγάλα κέρδη τις περισσότερες φορές, βασίζονται στον όγκο. Οι επιτιθέμενοι ελπίζουν ότι τελικά κάποιος θα κάνει ένα μεγάλο λάθος. Συνολικά, η μέθοδος ανίχνευσης λειτουργούσε καλά και μπορεί να αποτελέσει μια σταθερή βάση για την κατασκευή εργαλείων που θα βοηθήσουν τους χρήστες να παραμείνουν ασφαλείς.

R. Sangeetha, M N Veena

"Smart contracts" are software documented on block chains under specific circumstances that control the allocation of assets between individuals. In a smart healthcare supply chain, product traceability is a major issue. Two enabling technologies in the smart healthcare supply chain that ensure product traceability and safeguard against data manipulation are block chain and smart contracts. A smart contract workflow must be developed and carried out in a block-chain-based supply chain in accordance with the input data. This paper has an objective function to meet the entire system as a parallel composition of smart contracts and users this paper analyze the behavior of smart contracts and a core language of programs with an essential set primitive. The experimental results show that the proposed method can accurately detect security vulnerabilities and logic flaws in smart contracts through formal verification and other analysis techniques before smart contracts are deployed.

Chen Yang Lin, Hui Zhao, Jipeng Liu

Abstract While Ethereum smart contracts provide users with transfer and transaction services, vulnerabilities in smart contracts are constantly damaging users’ property and user experience. At present, many detection methods for smart contract vulnerabilities have been proposed, but these methods have not fully analyzed the information of multiple modalities of smart contracts, and their effectiveness in detecting long smart contracts is not ideal. We propose a lightweight Ethereum smart contract vulnerability detection method based on bimodal and hierarchical attention to address this issue. This method can combine the source code and opcode of smart contracts for analysis, and use a hierarchical attention network composed of bidirectional GRU and attention mechanism for vulnerability feature extraction. The experimental results show that in the task of detecting vulnerabilities in long smart contracts, this method has better detection capabilities for four types of vulnerabilities: Denial of Service, Reentrancy, Arithmetic, and Timestamp Dependency, compared to the most advanced deep learning smart contract vulnerability detection methods currently available.

Abdur Rehman Raza, Zuha Sohail, Khawir Mahmood, Shahzaib Tahir · 6 authors

Decentralized Finance (DeFi) has revolutionized financial transactions (peer-to-peer fund transfer) through blockchain-based smart contracts. However, vulnerabilities in smart contracts have caused financial losses exceeding billions of dollars, underscoring the need for robust security measures. This paper addresses the critical research questions about smart contract vulnerabilities, their real-world exploitation, and the effectiveness of vulnerability detection tools. We comprehensively analyze the four most critical smart contract vulnerabilities using Solidity code examples, real-world attack analyses, and audit findings. We then present mitigation strategies to help developers build secure decentralized applications (DApps). We also benchmark two widely used smart contract analysis tools, Slither, and 4naly3er, for accuracy and coverage. Our study reveals that Slither is more accurate, but no single tool provides complete coverage. This highlights the need for a multi-tool approach and manual audits for robust security.

Wenjuan Lian, Xinze Zhang, Zikang Bao, Bin Jia

ABSTRACT Background With the popularization and application of blockchain technology, smart contracts, as one of the underlying important technologies, have naturally attracted the attention of all parties. The vulnerabilities in smart contracts will lead to information leakage, asset theft, and other problems. Motivation Existing smart contract vulnerability detection tools mostly detect vulnerabilities through a set expert mode, relying more on professional knowledge. Traditional smart contract vulnerability detection methods based on deep learning rarely pay attention to syntactic information and semantic information at the same time, and their accuracy is low. Although the method based on graph neural network alleviates this problem to some extent, it suffers from the problem of too many nodes. Methods In this paper, we propose SNOW, an advanced method for detecting smart contract vulnerabilities, which leverages statement‐level joint feature graph and hybrid graph neural network to enhance the performance and efficacy of identifying smart contract vulnerabilities. Our proposed method consists of three parts. First, we generate a new graph representation called the Joint Feature Graph (JFG), which more effectively captures code information. Next, we introduce a hybrid graph neural network designed to extract JFG graph vectors more efficiently. Finally, we classify the graph vectors. Results We have conducted extensive experiments on two datasets and compared various existing methods. The results show that our method is superior to the current state‐of‐art method in many indexes such as accuracy and precision.

K. Siam, Bilash Saha, Md Mehedi Hasan, Md Jobair Hossain Faruk · 8 authors

Blockchain technology has emerged as a transformative innovation, providing a transparent, immutable, and decentralized platform that underpins critical applications across industries such as cryptocurrencies, supply chain management, healthcare, and finance. Despite their promise of enhanced security and trust, the increasing sophistication of cyberattacks has exposed vulnerabilities within blockchain ecosystems, posing severe threats to their integrity, reliability, and adoption. This study presents a comprehensive and systematic review of blockchain vulnerabilities by categorizing and analyzing potential threats, including network-level attacks, consensus-based exploits, smart contract vulnerabilities, and user-centric risks. Furthermore, the research evaluates existing countermeasures and mitigation strategies by examining their effectiveness, scalability, and adaptability to diverse blockchain architectures and use cases. The study highlights the critical need for context-aware security solutions that address the unique requirements of various blockchain applications and proposes a framework for advancing proactive and resilient security designs. By bridging gaps in the existing literature, this research offers valuable insights for academics, industry practitioners, and policymakers, contributing to the ongoing development of robust and secure decentralized ecosystems.

Pankaj Bhambri, Marta Starostka-Patyk

The amalgamation of artificial intelligence (AI) and blockchain technology has surfaced as an innovative remedy to bolster cybersecurity and mitigate threats, especially in decentralized settings. This chapter examines the synergistic integration of AI and blockchain to establish resilient, secure, and transparent systems for identifying and alleviating cyber threats. The capacity of AI to scrutinize extensive datasets and detect anomalies is augmented by blockchain&s;s decentralized, immutable, and transparent framework. This chapter commences with an examination of the obstacles in cybersecurity, especially within decentralized networks, and the necessity for sophisticated solutions. It subsequently explores the technical foundations of AI and blockchain, analyzing the integration of AI algorithms, including machine learning and deep learning models, with blockchain to improve threat detection, automate responses, and ensure data integrity. Use cases from various industries, including finance, healthcare, and critical infrastructure, demonstrate how this integration can provide scalable, efficient, and adaptive security solutions. The chapter also discusses emerging trends such as the role of smart contracts in security automation, the potential of decentralized AI models, and the future of blockchain-based AI-driven threat prevention. Finally, the chapter provides recommendations for practitioners on implementing these technologies and highlights future research opportunities in this rapidly evolving field.

Zhijun Wu, Hanwen Xu, Meng Yue, Yanrong Lu

No abstract is available for this record.

Butrus Mbimbi, David Murray, Michael Wilson

This research presents a novel framework and experimental results that combine zero-knowledge proofs (ZKPs) with private blockchain technology to safeguard whistleblower privacy while ensuring secure digital evidence submission and verification. For example, whistleblowers involved in corporate fraud cases can submit sensitive financial records anonymously while maintaining the credibility of the evidence. The proposed framework introduces several key innovations, including a private blockchain implementation utilising proof-of-work (PoW) consensus to ensure immutable storage and thorough scrutiny of submitted evidence, with mining difficulty dynamically aligned to the sensitivity of the data. It also features an adaptive difficulty mechanism that automatically adjusts computational requirements based on the sensitivity of the evidence, providing tailored protection levels. In addition, a unique two-phase validation process is incorporated, which generates a digital signature from the evidence alongside random challenges, significantly improving security and authenticity. The integration of ZKPs enables iterative hash-based verification between parties (Prover and Verifier) while maintaining the complete privacy of the source data. This research investigates the whistleblower’s niche in traditional digital evidence management systems (DEMSs), prioritising privacy without compromising evidence integrity. Experimental results demonstrate the framework’s effectiveness in preserving anonymity while assuring the authenticity of the evidence, making it useful for judicial systems and organisations handling sensitive disclosures. This paper signifies notable progress in secure whistleblowing systems, offering a way to juggle transparency with informant confidentiality.

Francesco Zola, Jon Ander Medina, A. Venturi, Raúl Orduna-Urrutia

Cryptocurrency users increasingly rely on obfuscation techniques such as mixers, swappers, and decentralised or no-KYC exchanges to protect their anonymity. However, at the same time, these services are exploited by criminals to conceal and launder illicit funds. Among obfuscation services, mixers remain one of the most challenging entities to tackle. This is because their owners are often unwilling to cooperate with Law Enforcement Agencies, and technically, they operate as 'black boxes'. To better understand their functionalities, this paper proposes an approach to analyse the operations of mixers by examining their address-transaction graphs and identifying topological similarities to uncover common patterns that can define the mixer's modus operandi. The approach utilises community detection algorithms to extract dense topological structures and clustering algorithms to group similar communities. The analysis is further enriched by incorporating data from external sources related to known Exchanges, in order to understand their role in mixer operations. The approach is applied to dissect the Blender.io mixer activities within the Bitcoin blockchain, revealing: i) consistent structural patterns across address-transaction graphs; ii) that Exchanges play a key role, following a well-established pattern, which raises several concerns about their AML/KYC policies. This paper represents an initial step toward dissecting and understanding the complex nature of mixer operations in cryptocurrency networks and extracting their modus operandi.

Pieter Hartel, Marianne Junger, Mark van Staalduinen

Abstract Objective This study examines Bitcoin generator (BG) websites on the clear and dark web. It focuses on their prevalence, revenue, and associated warnings, as these sites are suspected scams. Method Data for the study was gathered from the Dark Web Monitor and Iknaio Cryptoasset Analytics. A four-step process was used to identify BG sites and their Bitcoin addresses from 2 million dark websites. Results We found 832 dark web BG sites. The monetary revenue from a dark web BG site is approximately 1/3 smaller per Bitcoin address than from a clear web BG site. There is a concentration of revenue at a few BG sites. Only 24% of Bitcoin addresses on dark web BG sites have ever had money deposited on them. On the dark web, the top three clusters of crypto addresses account for 35% of the total revenue. On the clear web, the top three clusters account for 52% of the total revenue. The longer BG sites are online, the higher the revenue. There are hardly any warnings against BG sites. Conclusion Our results fit the Rational Choice model of crime: the revenue is modest, but the effort of the offenders is also limited.