Abstract In the blockchain world, proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger. The hashrate, a measure of the computational power directed toward securing a blockchain through proof-of-work consensus, is a fundamental measure of preventing various attacks. This study tests the causal relationship between the hashrate and the security outcome of the Bitcoin blockchain. We use vector error correction modeling to analyze the endogenous relationships between the hashrate, Bitcoin price, and transaction fee, revealing the need for an additional variable to achieve our aim. Employing a measure summarizing the growth of demand factors in the Bitcoin ecosystem indicates that hashrate fluctuations significantly influence security level changes. This result underscores the importance of the hashrate in ensuring the security of the Bitcoin blockchain.
Amit A. Rajdev, Amit Raninga, Kaushal Bhatt, Mr. Jeet Madhani
Cryptocurrency, as subset of virtual currencies, does not require presence of central authority and works on peer-to-peer exchange system. The speculative nature of cryptocurrency attracts many investors. The market for cryptocurrency is characterized by exponential growth and high volatility. The objective of this paper is to analyse the factors affecting acceptance of cryptocurrency in India by extending technology acceptance model (TAM). Using structured questionnaire, the data were collected from 269 respondents from Gujarat. Multiple linear regression was used for analysis purpose. The findings affirm that social influence, perceived trust and perceived ease of use were major predictors of the acceptance of cryptocurrency in India. However, perceived usefulness and regulatory support were not significantly associated with intention to invest in cryptocurrencies. The study provides valuable information to service providers based on survey results.
The entry of new technological infrastructures into the financial markets poses serious concerns about the misuse of the economic system for illicit purposes, such as money laundering and financing of terrorism. Although there are cases in which this connection has already been discovered by malicious actors, distributed ledger technologies can nevertheless represent a powerful tool at the disposal of competent authorities to trace illicit flows and to better monitor risks in financial markets. However, this possibility may go through an interdisciplinary analysis of the phenomena. The search for alternative systems to move funds, rather than the traditional financial intermediaries, such as banks, is not a new circumstance and not necessarily for criminal purposes. Nevertheless, some of the already-known value transfer systems may benefit from the use of distributed ledger technology and make their detection more difficult. The European institutions are discussing the needed legislative packages to enforce the current regulations and to extend their application to the crypto space, as well as the establishment of a new competent authority.
Izdehar M. Aldyaflah, Wenbing Zhao, Shunkun Yang, Xiong Luo
Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the smart contracts for analysis have also been proposed. However, the impact of these input methods has not been systematically studied, which is the primary goal of this paper. In this preliminary study, we experimented with four common types of input, including Word2Vec, FastText, Bag-of-Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). To focus on the comparison of these input types, we used the same deep-learning model, i.e., convolutional neural networks, in all experiments. Using a public dataset, we compared the vulnerability detection performance of the four input types both in the binary classification scenarios and the multiclass classification scenario. Our findings show that TF-IDF is the best overall input type among the four. TF-IDF has excellent detection performance in all scenarios: (1) it has the best F1 score and accuracy in binary classifications for all vulnerability types except for the delegate vulnerability where TF-IDF comes in a close second, and (2) it comes in a very close second behind BoW (within 0.8%) in the multiclass classification.
Jaime Govea, Walter Gaibor-Naranjo, William Villegas-Ch
Currently, in the digital era, critical infrastructure is increasingly exposed to cyber threats to their operation and security. This study explores the use of blockchain technology to address these challenges, highlighting its immutability, decentralization, and transparency as keys to strengthening the resilience of these vital structures. Through a methodology encompassing literature review, use-case analysis, and the development and evaluation of prototypes, the effective implementation of the blockchain in the protection of critical infrastructure is investigated. The experimental results reveal the positive impact of the blockchain on security and resilience, presenting a solid defense against cyber-attacks due to its immutable and decentralized structure, with a 40% reduction in security incidents. Despite the observed benefits, blockchain integration faces significant challenges in scalability, interoperability, and regulations. This work demonstrates the potential of the blockchain to strengthen critical infrastructure. It marks progress towards the blockchain’s practical adoption, offering a clear direction for future research and development in this evolving field.
This conceptual paper delves into the intriguing intersection between Ponzi schemes and cryptocurrency, shedding light on the unique dynamics, challenges, and implications arising from their convergence. While Ponzi schemes have long been recognized as fraudulent investment schemes, the emergence of cryptocurrency has introduced novel avenues for perpetrating such schemes. This paper examines the underlying mechanisms that facilitate the fusion of Ponzi schemes and cryptocurrency. It explores the emergence, characteristics, impact, and regulatory challenges. This paper contributes to a deeper understanding of the complex dynamics at play and provides insights into safeguarding investors and promoting the integrity of cryptocurrency markets.
Cryptocurrencies are becoming increasingly important for the modern economy. Prior literature focuses on aligning actor incentives to ensure the secure and efficient operation of cryptocurrencies against adversarial threats that are unobserved in the wild. In this work, we address the gap between the theory and practice of cryptocurrencies by advancing realistic approaches to analyze the economics and security of key cryptocurrency components: consensus mechanisms, transaction fee mechanisms (TFMs), and the application layer. We present novel models of these components that we evaluate both theoretically and using cryptocurrency clients. We augment our evaluation with the first evidence of an in-the-wild attack on a major cryptocurrency, highlighting our approach's practicality. Results contained in our work were adopted by cryptocurrency platforms that hold user assets worth over 300 billion.
Decentralized Finance, known as DeFi (Decentralized Finance), refers to the use of blockchain and digital assets or crypto-assets for the provision of financial services. Through DeFi applications, services such as loans, insurance, crypto-asset exchanges, among others, are offered, and are structured based on crypto-assets and technologically decentralized applications. This paper will discuss the concept of DeFi and how it challenges the traditional market infrastructures of the financial sector, demystifying the idea of absolute decentralization, generally mentioned in the crypto-asset arena. Subsequently, the paper will analyze the opportunities and challenges of DeFi for consumers, financial institutions, new competitors and financial regulators. Among the opportunities, it will present how DeFi could contribute to financial inclusion, to the automation of certain financial products, and how it is a key factor for the development of metaverses. As part of the challenges, the paper will analyze the problems of money laundering and terrorist financing in these markets, financial consumer protection, corporate governance issues, the lack of transparency of these products, cybersecurity risks and systemic risk. Finally, the paper will address some early regulatory responses that policymakers have proposed in response to some of these challenges in different jurisdictions.
Sabuj Saha, Ahmed Rizvan Hasan, Alvi Mahmud, Nujhat Ahmed · 6 authors
The use of cryptocurrency for financial crimes has increased in recent years because of its decentralized and anonymous nature. This study extracted scholarly articles from the Scopus database and adopted bibliographic and content analysis to review financial fraud research in cryptocurrency. In addition, this study discussed the top ten cryptocurrency scams, potential reasons for falling into those traps, and associated theories to explore scammers’ behavior and outlined comprehensive future research guidelines for a safer financial world. Since 2018, the publication trend of revealing cryptocurrency frauds has gained momentum, and research on this topic has increased significantly in the last two years. The USA is the most significant contributor to cryptocurrency scam research. We found that both developed and developing countries are fairly concerned about combatting crypto fraudsters even though there are no regulated guidelines across the countries. The research potential has shifted from malware, bitcoin, and blockchain to fintech-based crimes such as money laundering, pump-and-dump schemes, and phishing. We observed that ICO fraud, money laundering, Ponzi schemes, phishing, darknet market transactions, ransomware, and pumps and dumps are some of the predominant crimes in crypto and that investor overconfidence, speculative expectations, low barriers to entry, decentralization, and anonymity are the primary reasons for crimes in cryptocurrency. This study suggests studying the socioeconomic impacts of cryptocurrencies, the necessity for standardized global regulation, and the integration of interdisciplinary research. Future research should emphasize exploring the innovation cycle in cryptocurrency assets, understanding cybercrime dynamics, guarding against crypto market manipulation, and developing automated scam prevention.
I explore how criminals use cryptocurrencies in ransomware operations and leverage the vulnerability of virtual currencies to evade legal restrictions and international scrutiny. I do so by examining three drivers of the ‘merger’ between ransomware and cryptocurrency. First, criminal groups have embraced cutting-edge technologies to make their attacks more effective and maximise benefits that cryptocurrency presents, which include the convenience of fast payment and money laundering and the ease of hacking the currencies themselves. Second, ransomware groups have exploited the legal vacuum in the widespread use of rapidly circulating monetary instruments. Finally, groups have adopted cryptocurrencies because states – primary regulators of international financial transactions – remain in such disagreement over the control of digital activities that they have failed to address problems associated with them. In sum, this article presents a set of technical, legal and political reasons why groups have incorporated crypto in their operations.
In this article, a case-law from the Republic of Moldova of money laundering through cryptocurrencies is analyzed. The case finally reached the Supreme Court of Justice. In this sense, the errors committed by the courts were highlighted and concrete solutions were offered. Also, the relevant normative framework was analyzed and some legislative oversights were pointed out. Last but not least, it was emphasized that in the Republic of Moldova the provision of services regarding virtual assets is prohibited.
Abstract Financial transactions involving cryptocurrencies have seen significant growth in recent years. Individuals and companies are using cryptocurrencies as a medium of exchange. This unstoppable process has brought opportunities for the cost-effective performance of financial transactions, but at the same time challenges for law enforcement institutions. A significant portion of cryptocurrency transactions are conducted outside the conventional banking financial system. Such transactions are difficult for law enforcement authorities to trace. There is a high possibility that cryptocurrencies will be used by money launderers to hide their identity and launder their proceeds of crime. Offshore Financial Centers, which apply a high degree of anonymity, are widely using cryptocurrencies. Countries and international institutions should take coordinated action to tackle money laundering in the field of virtual currencies as it is very difficult for one country, alone, to fight the phenomenon of money laundering in the world of cryptocurrencies. The study focuses on the difficulties faced by financial institutions and law enforcement authorities in tracking cryptocurrency transactions in the context of prevention of money laundering, seen from the Albanian perspective.
The project titled “Rise Together: A Crowdfunding platform” introduces a groundbreaking Crowdfunding Platform on the Ethereum blockchain, revolutionizing traditional fundraising models. Leveraging smart contracts for seamless automation and execution of campaigns, the platform ensures transparency, security, and efficiency. Ethereum's decentralization eliminates intermediaries, reducing costs, and enhancing accessibility. Key features include a user-friendly interface, contribution tracking, and real time campaign monitoring. Utilizing Ether as the native cryptocurrency streamlines contributions, offering a secure and standardized means of support. Positioned at the intersection of blockchain and fundraising, this project contributes to the evolution of decentralized finance, showcasing Ethereum's potential for secure, inclusive, and innovative crowdfunding. Key Words: Rise Together, Crowdfunding, Blockchain, Smart Contract, Transparency, Decentralization, Wallet Integration.
Zhiyang Chen, Ye Liu, Sidi Mohamed Beillahi, Yi Li · 5 authors
Smart contract transactions associated with security attacks often exhibit distinct behavioral patterns compared with historical benign transactions before the attacking events. While many runtime monitoring and guarding mechanisms have been proposed to validate invariants and stop anomalous transactions on the fly, the empirical effectiveness of the invariants used remains largely unexplored. In this paper, we studied 23 prevalent invariants of 8 categories, which are either deployed in high-profile protocols or endorsed by leading auditing firms and security experts. Using these well-established invariants as templates, we developed a tool Trace2Inv which dynamically generates new invariants customized for a given contract based on its historical transaction data. We evaluated Trace2Inv on 42 smart contracts that fell victim to 27 distinct exploits on the Ethereum blockchain. Our findings reveal that the most effective invariant guard alone can successfully block 18 of the 27 identified exploits with minimal gas overhead. Our analysis also shows that most of the invariants remain effective even when the experienced attackers attempt to bypass them. Additionally, we studied the possibility of combining multiple invariant guards, resulting in blocking up to 23 of the 27 benchmark exploits and achieving false positive rates as low as 0.32%. Trace2Inv outperforms current state-of-the-art works on smart contract invariant mining and transaction attack detection in terms of both practicality and accuracy. Though Trace2Inv is not primarily designed for transaction attack detection, it surprisingly found two previously unreported exploit transactions, earlier than any reported exploit transactions against the same victim contracts.
Luo Feng, Ruijie Luo, Ting Chen, Ao Qiao · 8 authors
Smart contracts are integral to blockchain's growth, but their vulnerabilities pose a significant threat. Traditional vulnerability detection methods rely heavily on expert-defined complex rules that are labor-intensive and dificult to adapt to the explosive expansion of smart contracts. Some recent studies of neural network-based vulnerability detection also have room for improvement. Therefore, we propose SCVHunter, an extensible framework for smart contract vulnerability detection. Specifically, SCVHunter designs a heterogeneous semantic graph construction phase based on intermediate representations and a vulnerability detection phase based on a heterogeneous graph attention network for smart contracts. In particular, SCVHunter allows users to freely point out more important nodes in the graph, leveraging expert knowledge in a simpler way to aid the automatic capture of more information related to vulnerabilities. We tested SCVHunter on reentrancy, block info dependency, nested call, and transaction state dependency vulnerabilities. Results show remarkable performance, with accuracies of 93.72%, 91.07%, 85.41%, and 87.37% for these vulnerabilities, surpassing previous methods.
Lien Thi Huong Nguyen, Hanh Hong Vu, Anh Phuong Le
Since its public introduction in 2009, Bitcoin has grown to be the most well-known cryptocurrency worldwide. There is still debate as to whether Bitcoin may be used as a hedge against other assets. The purpose of this study is to investigate the correlation between Bitcoin and conventional commodity markets such as gold, crude oil, stock markets, and investor interest (quantified via Google Trends). In addition, the paper also tests Bitcoin’s safe haven role compared to other commodity markets. The Vector Autoregression model using daily database collected during the period 2013–2021 is employed to investigate the relationship between Bitcoin and traditional commodity markets. The impulse response function is used to analyze Bitcoin price movements against economic shocks from gold, oil prices, and the Dow Jones Industrial Average. In addition, the value-at-risk (VaR) model is used to test Bitcoin’s safe-haven property compared to other conventional commodity markets. The research results show that Bitcoin has negative impacts on gold, crude oil prices, and the stock market. Besides, Bitcoin responds negatively to a sharp decline in investor interest. Furthermore, the results of the VaR model show that Bitcoin is the second most volatile and risky asset, only after the crude oil market, and much riskier than gold. This result proves that Bitcoin cannot yet be considered a safe-haven instrument. These findings have several implications for investors and policymakers to minimize the risks associated with this cryptocurrency. AcknowledgmentThe authors would like to send their sincere thanks to the Reviewers and Editorial Board of the Journal. Their valuable comments and helpful support helped improve the paper’s quality. No funding was granted for this study.
The paper's recognition of the emerging phenomenon of cryptocurrencies. The rise of cryptocurrencies’ value on the market and the growing recognition around the arena open some demanding situations and concerns for business and commercial economics. The studies changed realized by way of the technique description, literature evaluation, and carried out research. This paper discusses the primary developments in the academic studies related to the Present Scenario of Cryptocurrency, a short overview of Cryptocurrency, cryptocurrencies through market capitalization, Cryptocurrencies Trending in Asia, Cryptocurrency in India, Cryptocurrency Exchanges, and cryptocurrency rules internationally. Keywords: Cryptocurrency, Bitcoin, Ethereum, Ripple, Virtual Currency, Blockchain *, Cyber Security, Blockchain Wallets, Distributed Ledger.
Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous services. As more financial institutions attempt to integrate cryptocurrencies into their networks, there is an increasing need to create a more transparent network that can withstand these kinds of attacks. In this work, we are using different classification techniques, such as logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) for Ethereum fraud detection. The dataset we are using includes rows of legitimate transactions done using the cryptocurrency Ethereum as well as known fraudulent transactions. The “XGBoost” model, which is noteworthy, detects variations that might attract notice and prevent potential issues in this chore.
Abstract The burgeoning interest in decentralized applications (Dapps), spurred by advancements in blockchain technology, underscores the critical role of smart contracts. However, many Dapp users, often without deep knowledge of smart contracts, face financial risks due to hidden vulnerabilities. Traditional methods for detecting these vulnerabilities, including manual inspections and automated static analysis, are plagued by issues such as high rates of false positives and overlooked security flaws. To combat this, the article introduces an innovative approach using the bidirectional encoder representations from transformers (BERT)‐ATT‐BiLSTM model for identifying potential weaknesses in smart contracts. This method leverages the BERT pre‐trained model to discern semantic features from contract opcodes, which are then refined using a Bidirectional Long Short‐Term Memory Network (BiLSTM) and augmented by an attention mechanism that prioritizes critical features. The goal is to improve the model's generalization ability and enhance detection accuracy. Experiments on various publicly available smart contract datasets confirm the model's superior performance, outperforming previous methods in key metrics like accuracy, F1‐score, and recall. This research not only offers a powerful tool to bolster smart contract security, mitigating financial risks for average users, but also serves as a valuable reference for advancements in natural language processing and deep learning.
T. Niedermayer, Pietro Saggese, Bernhard Haslhofer
The integration of bots in Distributed Ledger Technologies (DLTs) fosters efficiency and automation. However, their use is also associated with predatory trading and market manipulation, and can pose threats to system integrity. It is therefore essential to understand the extent of bot deployment in DLTs; despite this, current detection systems are predominantly rule-based and lack flexibility. In this study, we present a novel approach that utilizes machine learning for the detection of financial bots on the Ethereum platform. First, we systematize existing scientific literature and collect anecdotal evidence to establish a taxonomy for financial bots, comprising 7 categories and 24 subcategories. Next, we create a ground-truth dataset consisting of 133 human and 137 bot addresses. Third, we employ both unsupervised and supervised machine learning algorithms to detect bots deployed on Ethereum. The highest-performing clustering algorithm is a Gaussian Mixture Model with an average cluster purity of 82.6%, while the highest-performing model for binary classification is a Random Forest with an accuracy of 83%. Our machine learning-based detection mechanism contributes to understanding the Ethereum ecosystem dynamics by providing additional insights into the current bot landscape.
Thomas Vinther Daugaard, Jakob Bisgaard Jensen, Robert J. Kauffman, Kwansoo Kim
Banks can reduce resources spent on anti-money laundering (AML) compliance with block-chain-based transaction infrastructure. We consider AML compliance as a superset of know-your-customer (KYC) and transaction monitoring capabilities. We conducted an instrumental case study with Danske Bank and Concordium, using internal documents and interviews that served as empirical data. We show how storing digital representations of verified IDs with a blockchain can automate tasks and reduce redundant verification in KYC onboarding. Blockchain transparency also improves identifying counterparties, determining funds sources, and creating alerts in transaction monitoring. These reduce time and labor costs for AML compliance, which may lead to smaller banks. When more banks commit to layer-1 blockchain technology, the benefits of blockchain-based AML will increase. We implemented an instrumental case study approach in this theory-based qualitative research and encourage ECRA readers to recognize that the emerging technology innovations we study in this article have not yet been widely adopted and implemented by financial services firms. We also include a theoretical model with study hypotheses to make the main constructs that we investigate easily understood by non-technical ECRA readers. The findings we have developed are consistent with early-stage exploration in our research context and are intended to encourage more well-developed empirical results as the passage of time permits such work to be undertaken. (213 words)
Purpose This paper aims to investigate technological innovations within the crypto space that have engendered novel financial crime risks and their potential utilization amidst geopolitical conflicts. Design/methodology/approach The theoretical paper uses an analysis of recent geopolitical events, with a key focus on using cryptocurrencies to undertake illicit activities. Findings The study found that cryptocurrencies and the innovations made within the crypto domain are used for both legitimate and illicit purposes, including money laundering, terrorism financing and sanction evasion. Originality/value This research contributes to understanding the critical role cryptocurrencies play amidst geopolitical conflicts and emphasizes the need for regulatory considerations to prevent their misuse. To the best of the authors’ knowledge, this paper is the first scholarly contribution that considers the evolving mechanisms afforded by cryptocurrencies amidst geopolitical conflicts in undertaking illicit activities.
We consider the execution of smart contracts on Bitcoin. There, every contract step corresponds to appending to the blockchain a new transaction that spends the output representing the old contract state, creating a new one for the updated state. This standard procedure requires the contract participants to pay transaction fees for every execution step. In this paper, we introduce a protocol that moves most of the execution of a Bitcoin contract off-chain. When all participants follow this protocol, they are able to save on transaction fees, drastically reducing them. By contrast, whenever adversaries try to disrupt the off-chain execution, any honest participant is still able to enforce the correct contract behaviour, by continuing its execution on-chain.
Munir Ahmad, Muhammad Idrees, Muhammad Saleem Qazi
Cryptocurrencies have become a preferred mode of terror financing. it is imperative to analyze the role of cryptocurrencies in empowering terrorist organizations especially in developing states. It takes on a more serious form in developing countries like Pakistan, which is fighting a battle against terrorism for the last few decades. Now digital financing has become an undeniable reality of the modern world, it is essential to devise a comprehensive strategy for breaking the nexus between terrorism and cryptocurrencies. This article intends to explore the answers to the questions that why cryptocurrencies are becoming a preferred mode for terror financing. It argues that the anonymity, decentralization and operational ease of cryptocurrencies make them a preferred medium for terrorist organizations. Therefore, enhancing institutional capacity by training human resources and making dedicated efforts at the national level will prove to be instrumental in delinking terrorism and cryptocurrencies. Moreover, close collaboration between international watchdogs and national institutions is essential in mitigating the threat of the use of cryptocurrencies for terrorism.