Parisa Salahi, Reem Shady, Iyad Abu Doush, Marwa Kandil
More people are relying on Cryptocurrencies, yet these currencies are not controlled by any central authority or governments. Bitcoin transactions' anonymity facilitates money laundry activities by cyber-criminals. Traditional Anti-Money Laundering solutions, often rule-based, suffer from high false positive rates, leading to substantial operational costs. Machine Learning (ML) algorithms offers efficient analysis and identification of abnormal patterns in vast amounts of data; thus predicts suspicious transactions. This paper evaluates the effectiveness of four different ML algorithms in classifying Bitcoin transactions as either licit, or illicit transactions. The ML models were trained on the Elliptic dataset of Bitcoin transactions categorized into real entities belonging to licit and illicit categories [1]. The models were evaluated by the confusion matrix, accuracy, and features importance study. The experimental results proved that Random Forest (RF) and Extreme Gradient Booster (XGBoost) models achieved the highest accuracy of 95.1 % and 95.6%, respectively. This indicates that RF and XGBoost models effectively managed the data's complexity and accurately predicted suspicious transactions.
Abstract This study examines the spillover of Bitcoin's jumps and diffusive variations to traditional assets using highâfrequency data. For our crossâasset analysis, we detect positive spillovers from Bitcoin to risk assets and negative spillovers to defensive assets. We also find evidence of positive jump and diffusion spillovers from Bitcoin to U.S. equity sectors, particularly the financials, technology, consumer discretionary, and communication services sectors. By examining the source of these risk transmissions, we show that these spillovers are exacerbated by increased economic exposures to blockchain and cryptocurrency technologies by U.S. companies. The empirical findings reveal that the price fluctuations of an unregulated asset such as Bitcoin can materially affect the price dynamics of regulated assets.
In this study, the author focuses on how cryptocurrency and blockchain technology can be used to improve delivery of international aid since traditional methods include problems such as corruption, inefficiency and lack of transparency. The main research question addresses the question of how cryptocurrency could enhance efficiency, enhance transparency, accountability, and fight corruption in the dispensation of aid. The authors performed a qualitative content analysis of data collected from multiple articles, reports, and case studies including World Food Programme and United Nations Children's Fund(UNICEF).The study shows that blockchainâs distributed digital ledger minimizes misappropriation of funds risk, maintains real-time tracking; cryptocurrencies, specifically stablecoins, are more effective in real and low-cost transactions. Nevertheless, the work also points to the threats evident in legal and regulatory frameworks, low levels of digital literacy, and technological constraints. In conclusion, if all the challenges are solved, the cryptocurrency will enable the complete redesign of the distribution of the funds for aid.
Purpose: This research explores how advanced digital strategies can be harnessed to combat cryptocurrency-enabled crimes, focusing on the use of blockchain analysis, artificial intelligence (AI), machine learning, and enhanced regulatory frameworks to detect, trace, and prevent illegal transactions on cryptocurrency platforms. Materials and Method: The study examines the challenges law enforcement and regulatory bodies face due to the pseudonymous nature of cryptocurrencies and their cross-border complexities. It analyses emerging digital tools that facilitate the de-anonymization of transactions and enable real-time monitoring of suspicious activities. Case studies from recent high-profile cryptocurrency crimes, such as the Silk Road shutdown and recent ransomware attacks, are utilized to highlight the effectiveness of these digital strategies. Findings: The findings indicate that a multi-layered approach, which combines technological innovations with global regulatory efforts, is essential for mitigating risks associated with cryptocurrency as a facilitator of cybercrime. Advanced analytics and regulatory techniques are identified as key resources for detecting and preventing illicit activities. Implications to Theory, Practice and Policy: The research demonstrates practical implications for law enforcement agencies in developing strategies that integrate advanced digital tools to improve their capabilities to manage and investigate cryptocurrency-related crimes. From a policy perspective, the study highlights the importance of creating adaptive regulatory frameworks that can evolve alongside cryptocurrency technology to effectively address the unique challenges it presents in combating cybercrime.
Manita Deepak Shah, R. Seranmadevi, Shruti Jose, Amit Kumar Tyagi
Blockchain technology (BT) has emerged as a transformative effect in the banking and finance sector, promising increased efficiency, transparency, and security in financial transactions and services. This paper provides a detailed overview of the applications, challenges, and opportunities of blockchain technology in the banking and finance industry. We discuss about the fundamentals of blockchain, highlighting its key features such as decentralization, immutability, and smart contracts. This chapter provides various use cases of blockchain in banking, including cross-border payments, trade finance, and digital identity verification. We discuss the potential impact of blockchain on financial intermediaries, regulatory compliance, and risk management. While acknowledging the promising benefits of blockchain, we also discuss the challenges and limitations, such as scalability issues and regulatory issues, that must be addressed for widespread adoption. This work serves as a valuable resource for researchers, etc., can found valuable information of the transformative potential of BT.
This chapter examines the relationship between Bitcoin and the Marxian theory of value, building upon recent papers that argue for Bitcoinâs association with Socialism. Drawing parallels between the two, both Bitcoin and Marxian theory seem to consider labour, or work, as the basis of value. A similar perspective can be found in Platonovâs novel, The Foundation Pit , written during the peak of productivity propaganda in the USSR in the 1920s. Is âminingâ for Bitcoins akin to ceaselessly excavating a pit, generating something devoid of use value, but ascribed economic value through an idealization of labour? Ultimately, this chapter claims that Bitcoin is more akin to attempting to sell bottles filled with exhaust fumes. If seen through the lens of the Marxian theory of labour, the value of the Bitcoin should be grounded on the concept of labour of nature.
Cryptocurrency, a form of virtual currency, is increasingly pervasive in the modern society. People can use Bitcoin, Ether or Dogecoin to buy a range of products and services with ease and convenience. Yet, beneath the popularity of cryptocurrency is an emerging socio-legal concern: money laundering. Using the information collected from web sources, this brief paper taps into how cryptocurrency money laundering works and what measures can be undertaken to curtail digital crime. In view of the extant information, the paper found that a mixer is required as a âmiddle manâ to convert identifiable crypto-tokens into unidentifiable clean ones thereby delivering them to new wallet(s). In this manner, the origins of these tokens can be obscured, and the new tokens can be accessed and used legally. Many platforms and channels can serve as the mixer to proceed with illegal activities, including casinos, dark web marketplaces, and p2p networks. In considering the ways to tackle such digital crime in jurisdictions with limited oversight, the paper proposes to adopt measures in the Anti-Money Laundering initiatives already implemented in Hong Kong, the United States, and Singapore â especially, the need to strengthen recordkeeping to enhance the traceability and trackability of cryptocurrencies. Meanwhile, laws and rules associated with digital crime shall also be reshaped for risk mitigation.
ABSTRACT Smart contracts are turingâcomplete computer programs running on blockchains. Like traditional programs, smart contracts are also vulnerable. However, unlike traditional programs, it is very difficult to modify smart contracts once they are deployed on the blockchain. Therefore, reducing potential vulnerabilities in contracts before deployment to the blockchain is very important. The existing smart contract detection tools mostly fail to fully consider the complex control flow relationships within smart contracts, leading to false positives and false negatives. To address these problems, we propose FlawCheck, a vulnerability detection tool based on symbolic execution. First, it compiles smart contract source code into bytecode. Then, it disassembles bytecode into an opcode sequence, building the dependencies of contract control flow. Next, it performs preliminary analysis on the information generated during the simulated execution on the Ethereum virtual machine to identify suspicious vulnerability paths. Finally, it detects these suspicious paths by using symbolic execution. We verified that FlawCheck can detect five types of smart contract vulnerabilities. Experimental results on a dataset of 13016 real contracts shows that FlawCheck has higher accuracy than other tools.
Elections and voting play a crucial role in the development of a democratic society, enabling the public to express their views and participate in the decision-making process. Voting methods have evolved from paper ballot systems to e-voting systems to preserve the integrity of votes, ensuring a secure, transparent, and verifiable process. Continuous efforts have been made to develop a secure e-voting system that eliminates fraud attempts and provides accurate voting results. In this paper, we propose the architecture of a blockchain-based e-voting system called VoteChain. Developed to support the existing voting system in the state of Palestine, VoteChain aims to provide secure e-voting with features such as auditability, verifiability, accuracy, privacy, flexibility, transparency, mobility, availability, convenience, data integrity, and distribution of authority. The work introduces a smart contract designed to meet the demands of e-voting, governing transactions, monitoring computations, enforcing acceptable usage policies, and managing data usage after transmission. The proposed system also adopts advanced cryptographic techniques to enhance security. VoteChain features a web-based interface to facilitate user interaction, providing protection against multiple or double voting to ensure the integrity of the election. Furthermore, VoteChain is designed with a user-friendly and easily accessible administrator interface for managing voters, constituencies, and candidates. It ensures equal participation rights for all voters, fostering fair and healthy competition among candidates while preserving voter anonymity. A comparative analysis demonstrates VoteChainâs advancements in privacy, security, and scalability over both traditional and blockchain-based e-voting systems.
Smart contracts, self-executing agreements directly encoded in code, are fundamental to blockchain technology, especially in decentralized finance (DeFi) and Web3. However, the rise of Ponzi schemes in smart contracts poses significant risks, leading to substantial financial losses and eroding trust in blockchain systems. Existing detection methods, such as PonziGuard, depend on large amounts of labeled data and struggle to identify unseen Ponzi schemes, limiting their reliability and generalizability. In contrast, we introduce PonziSleuth, the first LLM-driven approach for detecting Ponzi smart contracts, which requires no labeled training data. PonziSleuth utilizes advanced language understanding capabilities of LLMs to analyze smart contract source code through a novel two-step zero-shot chain-of-thought prompting technique. Our extensive evaluation on benchmark datasets and real-world contracts demonstrates that PonziSleuth delivers comparable, and often superior, performance without the extensive data requirements, achieving a balanced detection accuracy of 96.06% with GPT-3.5-turbo, 93.91% with LLAMA3, and 94.27% with Mistral. In real-world detection, PonziSleuth successfully identified 15 new Ponzi schemes from 4,597 contracts verified by Etherscan in March 2024, with a false negative rate of 0% and a false positive rate of 0.29%. These results highlight PonziSleuth's capability to detect diverse and novel Ponzi schemes, marking a significant advancement in leveraging LLMs for enhancing blockchain security and mitigating financial scams.
Ensuring the absence of vulnerabilities or flaws in smart contracts before their deployment is crucial for the smooth progress of subsequent work. Existing detection methods heavily rely on expert rules, resulting in low robustness and accuracy. Therefore, we propose EDSCVD, an enhanced deep learning vulnerability detection model based on dual-channel networks. Firstly, the contract fragments are preprocessed by BERT into the required word embeddings. Next, we utilized adversarial training FGM to the word embeddings to generate perturbations, thereby producing symmetric adversarial samples and enhancing the robustness of the model. Then, the dual-channel model combining BiLSTM and CNN is utilized for feature training to obtain more comprehensive and symmetric information on temporal and local contract features.Finally, the combined output features are passed through a classifier to classify and detect contract vulnerabilities. Experimental results show that our EDSCVD exhibits excellent detection performance in the detection of classical reentrancy vulnerabilities, timestamp dependencies, and integer overflow vulnerabilities.
This chapter delves into the intricate relationship between digital assets, specifically non-fungible tokens (NFTs), and the regulatory landscape of anti-money laundering (AML) and counter financing of terrorism (CFT). With the rapid emergence of NFTs, new challenges and opportunities have arisen, necessitating an exploration of evolving regulatory frameworks and enforcement measures to combat AML and CFT risks associated with digital assets. This chapter focuses on the unique characteristics of NFTs, AML, and CFT risks within the NFT market, global regulatory developments, compliance challenges, technological solutions, enforcement actions, collaborative efforts, and future trends. By analyzing these aspects, this chapter aims to provide insights for policy-makers, regulators, scholars, and industry participants in effectively addressing financial crime risks in the digital asset landscape.
Crypto-Currency has witnessed a growing trend of adoption and skyrocketing market size in recent years, imposing significant impacts and challenges to financial industries. One of the challenges that attract intensive interests from academia and industries is money laundering with crypto-currency, due to such regulation-resistant features as decentralization, anonymity and faster trading speed. The existing works propose to address this anti-money laundering (AML) issue by optimizing an individual machine learning algorithm or simply ensembling homogeneous models with fixed rules, and thus may fail in adapting to various AML scenarios in dynamic and open crypto-currency markets. In this paper, we aim to tackle this problem by proposing a novel blockchain-based ensemble learning framework for anti-money laundering(BELFAL) in crypto-currency markets. BELFAL can improve prediction performance of AML tasks through parallel, collaborative ensembling of multiple heterogenous models, and leverage blockchain and smart contracts for flexible scheduling of ensemble rules. We establish a preliminary prototype based on Ethereum and Inter-Planetary File System (IPFS) to evaluate our framework, and conduct experiments on Elliptic, an opensource dataset of Bitcoin transactions. The results verify that our ensemble model and framework outperform individual models, and can help realize flexible scheduling of models.
This study examines the legal challenges associated with the commercialization of non-fungible tokens (NFTs) in Europe and Brazil. This paper provides a comprehensive analysis of the European and Brazilian legal frameworks, identifying key legal challenges related to intellectual property rights, consumer protection, taxation, and anti-money laundering (AML) regulations. Through a comparative analysis, we highlight the similarities and differences between the two jurisdictions, as well as best practices for addressing these legal challenges. The paper also discusses recent developments and court decisions, demonstrating the evolving legal landscape for NFTs in both regions. The findings of this paper have significant implications for the future of NFTs in Europe and Brazil, as well as for the broader digital economy, and offer valuable insights for policymakers, legal professionals, and market participants. Additionally, the paper identifies areas for further research, including the impact of technological advancements, the role of smart contracts, cross-jurisdictional issues, and the relationship between NFTs and traditional intellectual property rights.
Marco Ortu, Giacomo Ibba, Giuseppe Destefanis, Claudio Conversano · 5 authors
The expansion of smart contracts on the Ethereum blockchain has created a diverse ecosystem of decentralized applications. This growth, however, poses challenges in classifying and securing these contracts. Existing research often separately addresses either classification or vulnerability detection, without a comprehensive analysis of how contract types are related to security risks. Our study addresses this gap by developing a taxonomy of smart contracts and examining the potential vulnerabilities associated with each category. We use the Latent Dirichlet Allocation (LDA) model to analyze a dataset of over 100,040 Ethereum smart contracts, which is notably larger than those used in previous studies. Our analysis categorizes these contracts into eleven groups, with five primary categories: Notary, Token, Game, Financial, and Blockchain interaction. This categorization sheds light on the various functions and applications of smart contracts in today's blockchain environment. In response to the growing need for better security in smart contract development, we also investigate the link between these categories and common vulnerabilities. Our results identify specific vulnerabilities associated with different contract types, providing valuable insights for developers and auditors. This relationship between contract categories and vulnerabilities is a new contribution to the field, as it has not been thoroughly explored in previous research. Our findings offer a detailed taxonomy of smart contracts and practical recommendations for enhancing security. By understanding how contract categories correlate with vulnerabilities, developers can implement more effective security measures, and auditors can better prioritize their reviews. This study advances both academic knowledge of smart contracts and practical strategies for securing decentralized applications on the Ethereum platform.
In 2021, President Nayib Bukele introduced Bitcoin as legal tender in El Salvador. This paper examines the relationship between right-wing populism and Bitcoin. Through textual and visual analyses, the paper shows how Bukele weaves the promises and techno-utopian ideology surrounding Bitcoin into classic populist narratives. Adopting Bitcoin adds the promise of technology-driven development and the realization of a techno-utopia to the populist narrative template, allowing Bukele to construct a populist myth of himself as a tech-savvy visionary. This brand of crypto-populism represents a novel addition to the right-wing populist repertoire, capitalizing on the promises of an ongoing digital revolution.
The objective of this research is to determine the impact of geopolitical developments on Bitcoin's value. It focuses on the events that occurred from October 7, 2023 including the attack on Israel by the militant group Hamas, the tension between Iran and Israel, and the conflict between Palestine and the US. Through a comprehensive event study, we can analyze the returns generated by these events. The results of the study Srevealed that Bitcoin performed well during the adjustment and anticipation periods, which showed that it could be a safe-haven asset. On the other hand, the negative AAR during the event day reflected the market's first reaction. The study also highlighted Bitcoin's dual nature as a speculative asset and a safe-haven asset providing investors with a deeper understanding of the risks that affect the cryptocurrency market.
Cedrick Agorbia-Atta, Imande Atalor, Rita Korkor Agyei, Richard Nachinaba
This study addresses the critical issue of terrorist financing through cryptocurrency platforms, a growing concern due to digital currencies' pseudonymous nature and global reach. The research explores the strategic role of Artificial Intelligence (AI) and Machine Learning (ML) in identifying, preventing, and disrupting the flow of illicit funds used to finance terrorism. Employing a mixed-methods approach, the study integrates qualitative case studies of documented instances of cryptocurrency-based terrorist financing with quantitative data analysis from significant cryptocurrency exchanges. Advanced AI and ML algorithms, including supervised learning models such as decision trees and neural networks, were applied to detect suspicious transactions indicative of terrorist activities. The findings reveal that AI and ML technologies significantly enhance the ability to identify patterns of terrorist financing within large and complex datasets, with models achieving precision and recall rates exceeding 90%. However, challenges remain, particularly regarding the quality and standardization of data across platforms, algorithmic biases, and the need for continuous updates to counter evolving tactics used by terrorist organizations. The study concludes that AI and ML present powerful tools for enhancing financial security. However, their successful implementation requires overcoming these challenges through collaborative efforts among stakeholders, including financial institutions, regulators, and technology providers. This research contributes to the growing field of economic crime prevention by offering a robust framework for integrating AI-driven solutions into the fight against terrorist financing on cryptocurrency platforms.