Dimitris Kafteranis, Hüseyin Ünözkan, Umut Türkşen
Non-Fungible Tokens (NFTs) is a new virtual asset phenomenon the trade of which has spread quickly without any regulationas no legislation has been adopted in the EU, USA or the UK where the majority of NFT trading takes place. Concerns have been raised about NFTs and their relation to fraud and money launderingasanonymity and price volatility of NFTs create a unique and profitable asset for criminals. This paper addressestwomain issues: (1) trading statistics on NFTs,their analysis,and if and to what extent NFTs are used for financial crimes purposes; and (2) the legal challenges posed by the misuse of NFTs for fraud and other economic crimes. The final section of this paper provides feasible regulatory and business solutions that can help businesses to mitigate risks emanating from NFTs. It is argued that legal scholars, businesses and/or regulators cannot solve the challenges and risks posed by NFTs on their own, requiringmultidisciplinary research from academia and knowledge exchange between private and public stakeholders to close this gap.
In order for tokenized asset networks to be accountable as Web3 marketplaces for token-related transactions, identity verification must be conducted by gateways into those token networks. This includes the identity validation and legal status verification of the originators and beneficiaries, the gateway owners/operators, and other relevant service providers. The classic identity provider model could be enhanced to support anti-money laundering regulations, notably the Travel Rule. A privacy-preserving IdP model in combination with a legal service provider is explored where the IdP becomes the issuer of a blinded attestation regarding the user attribute, and where the legal representative with attorney-client privilege becomes the first point of contact for requests for the disclosure of the blinded attestations.
The derivative trading market is a critical component of the global financial system, and its efficiency and fairness directly affect market participants. The adoption of blockchain and smart contracts in this domain has the potential to revolutionise the way derivatives are traded, benefiting both businesses and investors. This research paper explores blockchain technology's and smart contracts' potential transformative impact on the derivative trading market. It aims to investigate how these technologies can enhance transparency, efficiency, and security while reducing fraud and errors in derivative trading. The study employs a literature review and analysis of current trends and developments in blockchain and smart contract technology. It also considers case studies and pilot projects in the financial industry to understand the practical implications. The research reveals that while the adoption of blockchain and smart contracts in the derivative trading market is in its early stages, there is a growing interest among businesses and investors. As the technology matures, it is likely to play an increasingly crucial role in enhancing the fairness and accessibility of derivative trading. The findings suggest that integrating blockchain and smart contracts in derivative trading can reshape the industry by improving transparency, efficiency, and security. Market participants and regulators should closely monitor these developments to ensure a fair and competitive marketplace. Additionally, businesses should consider the cost-saving opportunities and strategic advantages of adopting these technologies in their derivative trading operations.
Money laundering has significantly increased in recent years as a result of the quick growth of financial systems and the emergence of cryptocurrencies. These crimes threaten the stability of both economic and social systems and impact the security of public and private financial institutions. In response, detecting and preventing money laundering has become a top priority for financial systems, which rely on deep analysis of assets and the nature of funds to uncover illegitimate sources. With the rise of cryptocurrencies like Bitcoin, criminals have adopted new technologies and channels to cover up their illegal activities. This research aims to develop a new deep learning architecture for detecting illegitimate Bitcoin transactions in order to prevent Bitcoin laundering crimes. In this study, a deep investigation of the previous works will be conducted for better understanding of the problem statement. Besides, a new framework will be designed to exceed the performance of current best practices with regard to accuracy. Ultimately, the findings of this research will aid the financial sector in combating Bitcoin laundering activities and strengthening the security of digital financial transactions. An elliptic dataset used for bitcoin transactions that belong to real entities, where it consists of 203,769 nodes and 234,355 edges for illicit and licit transactions. The licit transaction families are licit services, exchanges, miners, and wallet providers. While the illicit transaction families are malware, frauds, Ponzi (fraud) schemes, ransomware, and terrorist organizations. The proposed model semi-supervised generative adversarial network (SGAN) utilized to complete the labelling process of the unknown entities. Based on the experimental results, the proposed model significantly improved upon previous methods, achieving a 98% success rate in accurately predicting illicit transactions. Thus, we consider it as a competitive stand in terms of anti-laundering for the Bitcoin cryptocurrency.
The increasing prevalence of Ethereum has led to a growing occurrence of illegal activities, including money laundering, the need to effectively detect money laundering transactions on the Ethereum platform has become crucial. However, there is currently limited research on detecting money laundering addresses within Ethereum. In this paper, we address this gap by proposing the use of heuristic algorithms to to solve the money laundering problem with a special focus on address detection. In this work, we collected nearly 7000,000 transaction records from Ethereum and introduced a heuristic algorithm based on cyclic transaction patterns to identify money laundering addresses primarily associated with such patterns. Our proposed algorithm analyzes transaction patterns and strategies in historical money laundering transaction data, providing valuable insights and revealing the behaviors and psychological changes of money launderers.
Decentralized Finance (DeFi) heralds a transformative moment in the realm of finance, challenging traditional intermediaries with a blockchain-centric blueprint. As DeFi burgeons, the intricate dance between its evolution and security emerges as an area of pivotal significance. This workshop navigates the multifaceted landscape of DeFi, where inherent challenges intertwine with new vulnerabilities, emphasizing the necessity for vigilant evaluations and adaptive measures to ensure the integrity of the ecosystem. It further delves into the ripple effects of regulatory scrutiny and its subsequent influence on DeFi's security matrix. As we stand on the cusp of uncharted territories, the workshop aims to provide a comprehensive discourse on DeFi's security challenges, fortified by interdisciplinary expertise, inviting participants to explore, ideate, and collaboratively forge a path towards a robust and secure DeFi paradigm.
Nov 14, 2023·2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Reentrancy is a type of attack that can occur in smart contracts, enabling untrusted external code execution within the contract. This method exploits a vulnerability that allows an attacker to repeatedly invoke a function in the contract, resulting in an infinite loop and potentially leading to fund theft. Therefore, the reentrancy attack represents a critical concern in blockchain security, prompting the development of various methods for analyzing and detecting reentrancy vulnerabilities over the last decade. Among these methods, the most recent ones leverage the advantages of AI and deep learning techniques. Nonetheless, several limitations persist in existing approaches. Many current methods rely on complex code analysis rules, resulting in a high number of false positives and false negatives. Additionally, the feature engineering process involving word embedding techniques can lead to the loss of critical information. Lastly, the majority of proposed methods necessitate access to the actual source code of the smart contracts for analysis. In this study, we introduce a straightforward and lightweight approach to address these limitations in reentrancy detection. Our approach employs an image-based detection method utilizing deep learning. The pipeline of our method involves disassembling the smart contracts into opcodes and transforming them into RGB images. These images are then used to train a VGG16 CNN model to detect similarities between images labeled as either “Vulnerable” or “Not Vulnerable”. To address class imbalance, we implement image augmentation techniques to expand the training dataset. Experimental results conducted on a publicly available dataset demonstrate that our model achieves a significantly high accuracy rate of 99.07%.
Cryptomarkets are increasingly requiring users to purchase products with Monero (a ‘privacy coin’) to further obfuscate the digital trail of money compared to conventional cryptocurrencies (e.g. Bitcoin). This is the first study to explore how cryptomarket communities are used to facilitate norms and behaviours to expedite these emerging cryptocurrency practices. Through a qualitative analysis of Monero threads in a Reddit cryptomarket community (3451 total posts), this research illustrates how online communities often underpin the adoption of new technologies in cryptomarkets. The findings reveal: how the online community functions, adapts, and fails to support cryptocurrency transitions; the appraisal and contestation of cryptocurrency risks; and the ideological drivers and symbolic resources used to align community practices to adopt Monero. This research contributes to an understanding of the processes that underpin the constant evolution of online illicit markets as human and non-human elements are constantly re-assembled.
This study presented a literature review on the main issue associated with the impact of Bitcoin on the financial market and the new technological development that has reformed market interaction within society. Currently, the usefulness of digital currency has expanded financial trading. Bitcoin is considered the most significant global cryptocurrency due to its significant market recognition and technological nature. Although various studies have explained the merits and demerits of Bitcoin, most studies emphasised its influence and relationship with the financial market. Hence, this study also addressed this gap. An instrument and concepts were provided to comprehend the dynamics of Bitcoin and determine its function in the financial market. Moreover, the global meaning and function of Bitcoin were examined. Virtual and previous literature reviews highlighted a strong relationship between Bitcoin and the financial market with other cryptocurrencies. Summarily, Bitcoin is still in the early stage and needs to be developed through technological growth.
Abstract The vulnerability in smart contracts (SCs) on the blockchain system may lead to severe security compromises. The SC can be invoked from an externally owned account (EOA) or a contract account (CA). The account a user creates to receive or send ether is an EOA. A CA contains codes that can interact with SCs. In Solidity SC, some vulnerabilities can only be exploited by the interactions between CAs and vulnerable SCs, which can be named external‐risky vulnerabilities . Most state‐of‐the‐art (SOTA) detectors detect external‐risky vulnerabilities by executing contract codes as an EOA user, thus reporting many unexploitable vulnerabilities. Therefore, we propose a CA‐triggering method to identify exploitable external‐risky vulnerabilities in Solidity SCs. We first designed agent contracts to simulate CAs' interactions with the target SCs in the real blockchain environment. We then detect vulnerability exploitation by analyzing transaction logs between agent contracts and target SCs and identifying successful exploits. We implemented the CA‐triggering method in a tool named SoliTester and evaluated it using three benchmark datasets, which contain three types of external‐risky vulnerabilities, namely, Reentancy (RE), Unchecked Call (UcC), and TxOrigin (TO). The results show that SoliTester can efficiently detect exploitable external‐risky vulnerabilities with significantly better precisions and recalls than SOTA detectors.
Smart contract vulnerability detection is an important security practice aimed at identifying and fixing potential vulnerabilities. This detection technique involves using static and dynamic analysis methods to inspect and test contract code, in order to identify code patterns and logical errors that may lead to security vulnerabilities. However, summarizing previous research reveals limitations in terms of scalability and generalizability, which can result in higher rates of false positives and false negatives in detection results. Therefore, we propose a novel smart contract detection framework called TSCSG: Two-Stage Smart Contract Vulnerability Detection Combining Semantic Features and Graph Features. In the graph extraction stage, TSCSG utilizes the data flow graph and control flow graph of smart contracts to extract the required contract graph. After processing the graph data, TSCSG employs our proposed RTMP network to extract smart contract graph features. In the semantic extraction stage of contract vulnerabilities, TSCSG utilizes smart contract data propagation chains to extract semantic features of smart contract vulnerabilities, which are then combined with the graph features to obtain the final detection results. Our large-scale empirical study on the EtherScan dataset demonstrates that TSCSG achieves satisfactory results in detecting reentrancy and timestamp vulnerabilities, outperforming 9 state-of-the-art vulnerability detection methods.
This paper provides a contextual analysis of the adoption of Bitcoin as legal tender in El Salvador. First, we outline the historical context and the political situation of the period 2019–24 that serve as context for the passage and implementation of the Bitcoin law (Decree No. 57). We identify the institutional and political context and the main areas of contention. Next, we delve into the macroeconomic context of El Salvador, outlining the fundamental features of its economy and highlighting how they relate to currency issues. Our analysis reveals that the adoption of Bitcoin cannot be understood without factoring in the mounting strains surrounding dollarization, remittances, and foreign debt. We conclude by putting forth a set of hypotheses regarding the potential dynamics and future of Bitcoin as legal tender in El Salvador that point beyond Bukele’s tactics.
Bitcoin has become a popular method for illegal transactions, such as ransomware payments and money laundering. Detecting these activities within the Bitcoin blockchain is challenging due to the lack of transaction labels and the network's enormous size, allowing bad actors to hide their actions. Previous studies have suggested using unsupervised anomaly detection or supervised and active learning techniques for identifying illicit activity within Bitcoin's network. This paper presents a novel machine-learning methodology that combines feature engineering with supervised learning algorithms to identify illicit transactions in the Bitcoin network. The approach shows promising results in accurately classifying transactions as illicit or legitimate. This method not only provides an efficient solution for detecting unauthorized transactions but also holds significant implications for establishing robust regulatory frameworks for digital currencies.
In the face of increasingly sophisticated cybercrime threats, large companies now need innovative solutions to protect their data and interests. Blockchain technology, which is based on the principles of decentralization and strong encryption, offers great potential in improving corporate cybersecurity. This research investigates the implementation of blockchain technology in the context of enterprise security by developing a blockchain-based dynamic system model. These findings make an important contribution in changing the way audits and general accounting operations are carried out, presenting fundamental changes in the profession. This new approach integrates blockchain technology into various aspects of cybersecurity, embracing innovation and creativity in the face of current challenges. By creating accurate computer models, this research brings a breakthrough in understanding system responses to employee fraud in corporate environments that adopt blockchain technology. This research aims to explore the potential of blockchain technology in improving corporate cybersecurity by identifying security gaps and designing effective updates, creating a safe and trustworthy digital environment for companies in this digital era. The findings of this research highlight the importance of integrating blockchain technology in auditing and general accounting operations, creating a foundation for the development of robust cybersecurity systems. In the context of companies using blockchain technology, this research reveals improved system responsiveness to employee fraud, indicating positive potential in mitigating security risks. This research provides a solid foundation for further development in the field of enterprise cybersecurity, inspiring innovation in protecting businesses and digital assets in a rapidly evolving cyberspace.
Abstract This chapter assesses the risks of money laundering and financing of terrorism (MLFT) via the Metaverse. This chapter starts in Sect. 4.2 by discussing the three traditional stages of MLFT in relation to the Metaverse. These stages are placement, layering and integration. In addition to the traditional stages of MLFT, the Metaverse poses additional risks to MLFT. The first additional risk identified in this book is the possibility of entering and creating realities fully anonymously. The threats of anonymity in the Metaverse will be discussed in Sect. 4.3.1. The second additional risk is that of the lack of jurisdiction in the Metaverse. This will be further explored in Sect. 4.3.2. The third and final additional risk relating to the Metaverse is that of the use of Non-Fungible Tokens (NFTs). These will be discussed in Sect. 4.3.3.
The emergence of the Bitcoin cryptocurrency marked a new era of illegal transactions. Cryptocurrency provides some level of anonymity allowing its users to create an unlimited number of wallets with alias addresses, which makes it challenging to identify the actual user. This is used by criminals for the purpose of making illegal transactions. At the same time, Bitcoin stores and provides information about all committed transactions, which opens up opportunities for identifying suspicious behavior patterns in this network using data mining. The problem of detecting suspicious activity in the Bitcoin network can be solved with sufficiently high accuracy using machine learning methods. The paper provides a comparative study of various machine learning methods to solve the mentioned problem: logistic regression, decision tree, random forest, gradient boosting.. Selecting hyper parameters, rebalancing the dataset, and active learning are particularly important. The most important hyperparameters of the algorithms are described. Metrics show that the gradient boosting looks the most promising. In total 38 features of bitcoin addresses were identified. The top features are presented in the paper.
Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witnessed the first de facto blockchain takeover that happened between Steem and TRON. Within one hour of this incident, TRON founder took over the entire Steem committee, forcing the original Steem community to leave the blockchain that they maintained for years. This is a historical event in the evolution of blockchains and Web 3.0. Despite its significant disruptive impact, little is known about how vulnerable DPoS blockchains are in general to takeovers and the ways in which we can improve their resistance to takeovers. In this paper, we demonstrate that the resistance of a DPoS blockchain to takeovers is governed by both the theoretical design and the actual use of its underlying coin-based voting governance system. When voters actively cooperate to resist potential takeovers, our theoretical analysis reveals that the current active resistance of DPoS blockchains is far below the theoretical upper bound. However in practice, voter preferences could be significantly different. This paper presents the first large-scale empirical study of the passive takeover resistance of EOSIO, Steem and TRON. Our study identifies the diversity in voter preferences and characterizes the impact of this diversity on takeover resistance. Through both theoretical and empirical analyses, our study provides novel insights into the security of coin-based voting governance and suggests potential ways to improve the takeover resistance of any blockchain that implements this governance model.
In recent years, blockchain technology has witnessed rapid development and received considerable attention. However, its decentralized and pseudonymous nature has also attracted many criminal activities. Among them, Ponzi schemes, a classic form of financial fraud, also hide their true face in smart contracts, causing huge losses to blockchain users. Although numerous methods have been proposed to detect Ponzi contracts, these methods still have limitations in terms of generalization and feature learning. To address this issue, we conduct research on Ethereum, the currently largest blockchain platform enabling smart contracts, and propose a novel contrastive learning-based smart Ponzi scheme detection method named ContraPonzi. This method first extracts control flow graph information from bytecodes and models it as attribute graphs that preserve both semantic and structural information. Next, by augmenting the bytecode data of multi-version compilers and maximizing the graph representation similarity of multi-version bytecodes of the same contract, a pre-training graph encoder is obtained and then can be used in Ponzi contract detection. Experimental results on real-world data demonstrate that ContraPonzi is significantly superior to the state-of-the-art in Ethereum Ponzi scheme detection.
The chapter addresses how the VASPs’ nature could lend itself to illicit uses, due to the technological progress that has allowed to protect the authors’ names of transactions with virtual currency. Background aspects such as the pandemic and the conflict between Russia and Ukraine may have accelerated the use of VASPs also in illicit terms. Therefore, regulation is necessary for the uncontrolled development of the illicit exploitation of a distributed ledger technology (DLT) as an upstream subject of VASPs. The role of the FAFT is essential to understand what the international orientation and commitment are in contrasting the cyberlaundering phenomenon, in which the VASPs represent one of the main players and a particularly attentive element to all international jurisdictions.
El Bitcoin es una criptomoneda creada en 2009 por su seudónimo creador Satoshi Nakamoto, es una moneda virtual e intangible, no la podemos tocar como a los billetes y monedas que se utilizan en el día a día, pero el Bitocoin también puede ser utilizado como un método de pago al igual que estos. Por medio de esta investigación se pretende dar a conocer los conceptos básicos sobre el Bitcoin, para que el público en general los conozca y ya no exista una desconfianza o desconocimiento hacia esta criptomoneda y hacia las demás criptomonedas que existen.