Blockchain Papers

Jason Scharfman

No abstract is available for this record.

Argyrios Koronaios, Georgia Koloniari

Bitcoin is a decentralized cryptocurrency, which is rapidly growing and offering many advantages. Although its structure protects users from some types of fraud, it is not completely immune, while fraud detection in Bitcoin remains still relatively unexplored. In this paper, we use a graph to model Bitcoin transactions and benefit from the graph’s structure to overcome the lack of informative transaction and user data. We utilize network analysis for feature extraction and model fraud detection as a classification problem using a Deep Neural Network as our classifier. Furthermore, we propose a novel approach that combines a Variational Graph Autoencoder (VGAE), for deriving appropriate node and graph embeddings, and supervised learning to detect fraudulent Bitcoin transactions. Our experimental results show that the proposed approach, while also affected by high class imbalance, similarly to using only the graph-based features for classification, performs significantly better in detecting high-risk areas in the graph.

Chris Gilbert, Mercy Abiola Gilbert

The rapid expansion of cryptocurrencies has revolutionized the digital economy, offering decentralized and secure transaction mechanisms through blockchain technology. However, this growth has concurrently attracted cybercriminal activities, exploiting the inherent anonymity and security features of cryptocurrencies for illicit purposes such as money laundering, ransomware, exchange hacking, tax evasion, Initial Coin Offering (ICO) frauds, Ponzi schemes, and phishing attacks. This paper provides a comprehensive analysis of cryptocurrency-related cybercrimes, identifying prevalent patterns and underlying vulnerabilities within blockchain systems and cryptocurrency exchanges. Utilizing a multifaceted research methodology that includes qualitative and quantitative analyses, case studies, and theoretical frameworks like the Martial Arts Matrix (MAM), the study elucidates the motivations and sophisticated tactics employed by cybercriminals. Key findings highlight the critical need for enhanced security measures, robust regulatory frameworks, and collaborative efforts among stakeholders to mitigate these risks effectively. Additionally, the paper explores emerging trends and technologies in blockchain security, such as decentralized identity management and quantum-resistant cryptographic algorithms, which hold promise for strengthening defenses against evolving cyber threats. The study concludes by offering actionable recommendations for law enforcement agencies, cryptocurrency providers, and policymakers to address the dynamic landscape of cryptocurrency-related cybercrimes, ensuring the sustained growth and trustworthiness of the cryptocurrency ecosystem.

Anagha Vinayak

No abstract is available for this record.

Vikas Kumar Jain, Meenakshi Tripathi

No abstract is available for this record.

Xiaoli Guo, Yanjun Zuo, Li Dong

Blockchain's decentralized characteristics have posed unique challenges and unlocked novel opportunities for the accounting and auditing sector. While the potential impact of blockchain and smart contracts on auditing has been raised, comprehensive studies remain scarce. Using the Solidity language, this study explores the viability of encoding into smart contracts specific auditing rules that can automatically identify suspicious transactions in common fraud schemes. To illustrate the feasibility, it presents a proof-of-concept framework encompassing system architecture, smart contract development, and workflow procedures. Simulation results demonstrate that blockchain-based smart contract approach in this study can effectively identify problematic transactions in near real-time. Consequently, this could help auditors to allocate audit resources to focus efforts on higher risk transactions. The findings provide implications for future studies on the application of smart contracts in auditing.

Mandeep Kumar, Bhaskar Mondal

No abstract is available for this record.

José Carlos Ramírez, Isaac Agudo

No abstract is available for this record.

Abayomi Titilola Olutimehin

This study evaluates the effectiveness of cybersecurity frameworks in mitigating cyber threats in traditional banking while assessing their applicability to Decentralized Finance (DeFi). Using financial sector reports, cybersecurity incident databases, and DeFi security audits, we analyze compliance with NIST CSF, ISO/IEC 27001, and PCI-DSS alongside factors such as bank size, IT security investments, and regulatory fines to determine their impact on cyber resilience. Logistic regression results indicate that compliance with cybersecurity frameworks reduces cyberattack likelihood (p = 0.0689, marginally significant), while larger institutions face fewer threats (p = 0.0256, statistically significant). However, increased IT security budgets paradoxically correlate with higher attack frequencies (p = 0.0385, statistically significant), suggesting larger attack surfaces may offset security investments. In contrast, DeFi faces disproportionately higher smart contract exploits, flash loan attacks, and oracle manipulation, leading to significantly greater financial losses (F = 216.92, p < 0.001, highly significant) than traditional banking cyber incidents. Regulatory compliance and industry collaboration show promise in reducing attack occurrences, with cyber incidents projected to decline by over 40% by 2029 under stricter enforcement. However, traditional frameworks are insufficient for DeFi’s decentralized structure, necessitating AI-driven threat detection, mandatory smart contract audits, secure oracle mechanisms, and adaptive regulatory frameworks. This study highlights the urgent need for tailored DeFi cybersecurity strategies while reinforcing the effectiveness of compliance-driven models in banking. It provides actionable insights for financial institutions, regulators, and cybersecurity professionals seeking to enhance resilience across centralized and decentralized financial systems.

Olaniyi Abiodun Ayeni, Ibitola Elizabeth Adejumo, David Adedamola Kolawole

Smart contracts face operates an open and transparent network and increased vulnerability to hacking and potential economic losses and large volumes of financial assets are stored, managed and operated on. Reentrancy attack is one of the main vulnerabilities of smart contracts. Reentrancy attacks exploit vulnerabilities in smart contracts that allow malicious actors to repeatedly re-enter […]

Austen D. Givens

The use of cryptocurrencies in transnational criminal activities has grown in recent years. The scholarly literature on cryptocurrencies recognizes this trend. Yet, there has been comparatively little attention paid to the degree to which cryptocurrencies pose a direct threat to U.S. homeland security interests. This article fills a gap in the scholarly literature on cryptocurrencies by presenting evidence that cryptocurrencies are a threat to U.S. homeland security interests, specifically because of their uses for financing terrorism, enabling human and drug trafficking, and evading international financial sanctions.

Andreas Karapatakis

The technological evolution has not only opened new frontiers but has also become an indispensable part of our daily lives. However, the technology that enhances our lives presents a dual reality—it offers opportunities for criminals while creating challenges for law enforcement. Fraud, particularly, has become a pervasive issue. In response, virtual asset service providers must take measures to tackle cryptocurrency-related fraud. Nevertheless, this becomes challenging if the perpetrator exists solely within the virtual world. In 1992, Neal Stephenson used the term ‘Metaverse’ to describe a virtual world where people interact with each other using avatars. Over time, the Metaverse has transformed into a complex concept akin to 'cyberspace'. The Metaverse is a virtual environment that uses technologies to mimic the real world. As this virtual space became intertwined with financial transactions, especially through cryptocurrencies, the Metaverse evolved into a medium for perpetrating scams. Within this context, the article addresses the challenges associated with criminal activity in the Metaverse. Considering the potential applications of AI, cryptocurrencies and Non-Fungible Tokens, three main challenges can be identified: 1) decentralisation, 2) anonymity of the user, and 3) lack of regulation. This article examines the applicability of existing legislation to regulate criminal activity in the Metaverse through doctrinal research. Using a comparative approach, it analyses the challenges of addressing virtual crimes by contrasting fraud (Fraud Act 2006) with sexual assault (Sexual Offences Act 2003), highlighting the complexity of addressing crimes involving physical contact in virtual spaces compared to financial crimes.

K P N V Satya Sree

This present study demonstrates the new methods of preventing financial fraud and cybercrime with the integration of blockchain technology in finance services from a regulatory framework, such as GDPR and PCI DSS. Blockchain provides decentralized and immutable ledger qualities which add to transparency and security in transactions, while GDPR and PCI DSS ensure strict compliance with standards for data protection. The proposed approach demonstrates a significant advantage in fraud detection, reduction of data breaches, and compliance efficiency and offers a robust framework for securing financial services in the digital era.

Rupali Gangarde

Blockchain technology plays a pivotal role in enhancing the security of financial systems, providing a robust framework to prevent cyber fraud. As cyber threats in financial transactions escalate, blockchain's decentralized and tamper-resistant nature offers an innovative solution for fraud mitigation. By leveraging distributed ledger technology (DLT), blockchain ensures transparency, traceability, and immutability in transactions, significantly reducing the risk of unauthorized alterations or manipulations. Smart contracts, a feature of blockchain, automate and secure transactions, minimizing human error and preventing malicious interventions. Additionally, consensus mechanisms like proof-of-work and proof-of-stake enhance security by requiring agreement from multiple nodes before validating a transaction, thus eliminating the risk of single points of failure. Financial institutions adopting blockchain can secure payment processing, authenticate identities, and prevent fraudulent activities such as double-spending or phishing attacks. Blockchain also ensures compliance with regulatory standards through real-time auditing and secure data sharing between financial entities. However, despite its advantages, challenges like scalability and regulatory acceptance remain. This paper explores the potential of blockchain in preventing cyber fraud within financial systems, highlighting its impact on security, trust, and fraud detection, while addressing existing challenges in adoption and implementation.

Ao Wang, Guojun Zhu, Jian Li

No abstract is available for this record.

Rusman Rusman, Zudan Arief Fakrulloh

Cryptocurrency investments are rapidly developing worldwide, including in Indonesia. Behind its profit potential, digital assets also open opportunities for criminals to commit money laundering offenses. The anonymity, pseudonymity, and decentralization of blockchain technology underlying cryptocurrencies create challenges for law enforcement in tracking illegal activities that exploit these assets. This study aims to examine the role of existing regulations in preventing the use of digital assets as a means of money laundering and to identify the challenges faced by law enforcement in enforcing rules against suspected cryptocurrency transactions. The research will analyze the extent to which the existing regulations, both at the national and international levels, are effective in preventing the use of cryptocurrencies for money laundering crimes. The second subtitle will explore various technical and legal constraints faced by law enforcement, including the lack of international cooperation, limitations of monitoring technology, and the low level of technical expertise among law enforcement officials.

Rossella Carletti, Xiaojun Luo, Ismail Adelopo

Purpose This paper aims to comprehensively examine the intricate relationship between cryptocurrency and criminal enterprises, shedding light on the methods, mechanisms and implications of cryptocurrency use in various financial crimes. Design/methodology/approach This paper conducts a thematic analysis of 51 cryptocurrency-related financial crime cases to identify criminogenic features of cryptocurrencies, key tools and features of cryptocurrency-related financial crimes. Based on the case study, policy-changing recommendations and big-data analytics tools are proposed for law enforcement agencies to combat cryptocurrency crimes. Findings This study found that decentralisation, pseudo-anonymity and borderless nature of cryptocurrencies enable cross-border financial flows and make transaction tracking a complex challenge. Popularity and market capitalisation used to play a dominant role in criminals’ choice of cryptocurrency as Bitcoin was associated with most cryptocurrency-related crimes before 2019. An increasing number of other cryptocurrencies, such as privacy coins and stablecoins, have been utilised for financial crimes recently. Cryptocurrency has also become an essential source of terrorist financing. Research limitations/implications Due to the open access to cryptocurrency transaction data, powerful big-data analytics tools should be developed to help law enforcement departments proactively detect cryptocurrency-related crimes. Practical implications It is important and urgent for cryptocurrency exchange companies, mixing services and wallet providers to implement strict anti-money laundering and know-your-customer measures to help combat cryptocurrency-related crimes and create a sustainable future for cryptocurrencies. Originality/value There is a lack of studies to reveal the current trend of cryptocurrency crimes; the relationship between criminogenic features of cryptocurrency and types of financial crimes; as well as the approaches used to facilitate different types of cryptocurrency-related financial crimes. These gaps will be addressed through thematic analysis on 51 crypto crime cases.

Shobhit Navani, Giuseppe T. Cirella

Cryptocurrency has emerged as a lucrative yet volatile landscape for cybercriminal activity, presenting novel challenges for law enforcement and policymakers alike. This review seeks to explore the diverse array of cybercrimes occurring within the cryptocurrency domain, examining their types, motives, techniques, and the regulatory responses shaping this complex ecosystem. Utilizing a scoping literature search methodology, this study analyzes 228 pertinent sources drawn from a pool of over 4,000 reviewed publications. The findings elucidate the intricate interplay between cryptocurrencies and illicit activities, revealing the multifaceted nature of cybercrimes within this realm. From the exploitation of the dark web for illicit transactions to the pervasive threat of crypto ransomware targeting entities globally, the review underscores the diverse methods and motivations driving such nefarious endeavors. By shedding light on the evolving tactics employed by cybercriminals and exploring future directions for technological and regulatory measures adopted by governments, this paper offers valuable insights to navigate this dynamic landscape effectively.

Tao Fang, Zhihao Hou, Jiahao He, Junjie Zhou · 5 authors

With the development of deep learning, especially driven by advanced models such as Graph Neural Networks (GNN), smart contract vulnerability detection is gradually moving toward automation and intelligence. Although existing deep learning detection methods have improved the efficiency of vulnerability detection to some extent, they fail to fully explore and utilize the rich syntactic and semantic information in smart contracts and generally suffer from insufficient feature extraction. In this paper, we propose a new method for smart contract vulnerability detection that combines a Multi-Information Contract Graph (MIG) with a Hierarchical Graph Feature Extraction model (HGFE). MIG integrates key information such as control flow, data flow, and vulnerability feature flow within smart contracts, fully mining and utilizing the rich syntactic and semantic features of smart contracts, providing the model with comprehensive feature representation. HGFE applies a multilayer feature extraction strategy, combining global and local feature extraction, and comprehensively considers multiple dimensions of information within the contract graph, thereby fully extracting the features of the contract graph. The experimental results demonstrate that our method significantly enhances the ability to detect potential vulnerabilities in smart contracts, achieving a maximum accuracy and precision of 97.29% and 97.70%, respectively, outperforming other advanced methods.

Jie Song, Sijia Zhang, Pengyi Zhang, Junghoon Park · 6 authors

In recent years, blockchain anonymity has led to more illicit accounts participating in various money laundering transactions. Existing studies typically detect money laundering transactions, known as AML (Anti-money Laundering), through learning transaction features on transaction graphs of transactional blockchains. However, transaction graphs fail to represent the accounts’ social features within transactional organizations. Account graphs reveal such features well, and detecting illicit accounts on account graphs provides a new perspective on AML. For example, it helps uncover illegal transactions whose transaction features are not distinct in transaction graphs, with a loose assumption that illicit accounts are likely involved in illegal transactions. In this paper, we propose a Social Attention Graph Neural Network ($\textsf {SGNN}$) on account graphs converted from transaction graphs. To detect illicit accounts,$\textsf {SGNN}$learns the social features on two sub-graphs, a heterogeneous graph and a hypergraph, extracted from the account graph, and fuses these features into account attribute vectors through attention. The experimental results on the Elliptic++ dataset demonstrate$\textsf {SGNN}$’s advances. It outperforms the best baseline by 14.18% in precision, 7.37% in F1 score, 0.96% in accuracy, and 0.64% in recall when detecting illicit accounts on account graphs, as well as detects 20.3% more recall of illegal transactions through these illicit accounts than state-of-the-art methods based on transaction graphs when the mappings between illegal transactions and illicit accounts are provided. Moreover, thanks to social features,$\textsf {SGNN}$has a novel capability that works under many account scales and activity degrees. We release our code onhttps://github.com/CloudLab-NEU/SGNN.

Anastasia Kassiani Blitsi, Eleftheria Katsoura, Georgios Stavropoulos, Konstantinos Votis

The rise of blockchain technology and cryptocurrencies such as Bitcoin and Ethereum has created new avenues for both lawful and illicit activities, including illegal firearm transactions. This study applies a combination of graph-based analysis, functional data techniques, and machine learning to detect and classify suspicious activities related to firearm trafficking on blockchain networks. A Random Forest model, achieving a precision of 0.907 and recall of 0.786, was used to identify illicit Bitcoin addresses, while a multi-target classifier categorized these addresses by specific types of illicit activity. For Ethereum, an XGBoost model achieved a precision of 0.9864 and an accuracy of 0.9901, demonstrating robust detection of suspicious accounts. Feature engineering and a rule-based system further enhanced model performance, though challenges remain in addressing misclassifications, particularly in distinguishing subtle transaction patterns. These findings underscore the potential of machine learning in blockchain forensics, providing critical insights for law enforcement efforts to combat illegal firearm trading.

Phuong Duy Huynh, Son Hoang Dau, Nicholas Huppert, Joshua Cervenjak · 8 authors

We explored the ubiquitous phenomenon of serial scammers, each of whom deployed dozens to thousands of addresses to conduct a series of similar Rug Pulls on popular decentralized exchanges. We first constructed two datasets of around 384,000 scammer addresses behind all one-day Simple Rug Pulls on Uniswap (Ethereum) and Pancakeswap (BSC), and identified distinctive scam patterns including star, chain, and major (scam-funding) flow. These patterns, which collectively cover about $40\%$ of all scammer addresses in our datasets, reveal typical ways scammers run multiple Rug Pulls and organize the money flow among different addresses. We then studied the more general concept of scam cluster, which comprises scammer addresses linked together via direct ETH/BNB transfers or behind the same scam pools. We found that scam token contracts are highly similar within each cluster (average similarities $>70\%$) and dissimilar across different clusters (average similarities $<30\%$), corroborating our view that each cluster belongs to the same scammer/scam organization. Lastly, we analyze the scam profit of individual scam pools and clusters, employing a novel cluster-aware profit formula that takes into account the important role of wash traders. The analysis shows that the existing formula inflates the profit by at least $32\%$ on Uniswap and $24\%$ on Pancakeswap.

Gaoxuan Li, Xinyu Tang

The rise of blockchain and cryptocurrency networks has fueled financial innovation, yet it also presents new opportunities for illicit activities such as fraud and money laundering. Traditional detection approaches struggle with the non-Euclidean, large-scale nature of cryptocurrency transaction networks. Graph Neural Networks offer a promising solution for capturing complex relational data. This paper proposes four fusion architectures—Triple Parallel Layer, Hierarchical Staging, Attention-Weighted Residual Fusion, and Multi-View Feature Aggregation—combining Graph Convolutional Network, Graph Attention Network, and Graph Isomorphism Network to enhance classification of illicit transactions. Experiments on the Elliptic Bitcoin dataset show that the proposed models achieve classification accuracies up to 97.17%, significantly outperforming standalone Graph Convolutional Network, Graph Attention Network, and Graph Isomorphism Network models. These results underscore the superior performance and robustness of the fusion architectures, with improvements in accuracy ranging from 1.1% to 2.9% over individual models, marking a step forward in financial crime detection within decentralized networks.

Monali Shetty, Sharvari Tamane

No abstract is available for this record.