Jamal Wiwoho, Irwan Trinugroho, Dona Budi Kharisma, Pujiyono Pujiyono
This article analyzes the negative impact of cryptocurrency mining activities on the environment and analyzes the urgency of cryptocurrency mining policy to protect the environment.This analysis was carried out by conducting a comparative study of regulations in countries open to the development of cryptocurrency, including Indonesia, the United States, China, and Iran.The results of this comparison are used as material for constructing environmentally friendly cryptocurrency mining regulations to be implemented in various countries.This type of research is legal research.The research approaches are the statutory, comparative, and case approaches.Data was collected using the literature study method, and technical data analysis was carried out using a qualitative juridical method.The results of this research are how to prevent and combat the negative impacts of cryptocurrency mining activities on the environment, including implementing several policies including minimizing greenhouse gas emissions, ensuring reliable energy, encouraging transparency and increasing environmental performance, data search to understand, monitor, and reduce impact, enactment of energy efficiency standards, besides that it is necessary to implement transaction fees and carbon taxes for cryptocurrency mining.
In recent years, Decentralized Finance (DeFi) has grown rapidly due to the development of blockchain technology and smart contracts. As of March 2023, the estimated global cryptocurrency market cap has reached approximately $949 billion. However, security incidents continue to plague the DeFi ecosystem, and one of the most notorious examples is the “Rug Pull” scam. This type of cryptocurrency scam occurs when the developer of a particular token project intentionally abandons the project and disappears with investors’ funds. Despite only emerging in recent years, Rug Pull events have already caused significant financial losses. In this work, we manually collected and analyzed 103 real-world rug pull events, categorizing them based on their scam methods. Two primary categories were identified:Contract-relatedRug Pull (through malicious functions in smart contracts) andTransaction-relatedRug Pull (through cryptocurrency trading without utilizing malicious functions). Based on the analysis of rug pull events, we propose CRPWarner (short forContract-relatedRugPull RiskWarner) to identify malicious functions in smart contracts and issue warnings regarding potential rug pulls. We evaluated CRPWarner on 69 open-source smart contracts related to rug pull events and achieved a 91.8% precision, 85.9% recall, and 88.7% F1-score. Additionally, when evaluating CRPWarner on 13,484 real-world token contracts on Ethereum, it successfully detected 4168 smart contracts with malicious functions, including zero-day examples. The precision of large-scale experiments reaches 84.9%.
The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address-transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others.
In the introductory part of the paper, the author briefly explores the emergence of the first cryptocurrency (Bitcoin), which was initially devised for the purpose of securing easier transactions without intermediaries. Criminals soon realised that cryptocurrencies, due to their inherent characteristics, could provide them with anonymity. As other cryptocurrencies (altcoins) emerged, it was necessary to define their conceptual framework. While cryptocurrencies were initially used in illegal sales of narcotics, their application soon spread to a number of other criminal activities. In that context, the author first presents the reasons that led criminals to turn to cryptocurrencies in their financial transactions, and then explains the possible uses of cryptocurrencies in the commission of crime. The central part of the paper provides examples of criminal activities committed by using cryptocurrencies. It is reasonable to expect that, in the future, the use of cryptocurrencies will extend to other criminal activities, which are still unaffected by the trend that has existed for the last ten years.
Clement Daah, Amna Qureshi, Irfan Awan, Savas Konur
As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model’s performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust.
Dimitris Karakostas, Aggelos Kiayias, Thomas Zacharias
We analyze bribing attacks in Proof-of-Stake distributed ledgers from a game theoretic perspective. In bribing attacks, an adversary offers participants a reward in exchange for instructing them how to behave, with the goal of attacking the protocol's properties. Specifically, our work focuses on adversaries that target blockchain safety. We consider two types of bribing, depending on how the bribes are awarded: i) guided bribing, where the bribe is given as long as the bribed party behaves as instructed; ii) effective bribing, where bribes are conditional on the attack's success, w.r.t. well-defined metrics. We analyze each type of attack in a game theoretic setting and identify relevant equilibria. In guided bribing, we show that the protocol is not an equilibrium and then describe good equilibria, where the attack is unsuccessful, and a negative one, where all parties are bribed such that the attack succeeds. In effective bribing, we show that both the protocol and the "all bribed" setting are equilibria. Using the identified equilibria, we then compute bounds on the Prices of Stability and Anarchy. Our results indicate that additional mitigations are needed for guided bribing, so our analysis concludes with incentive-based mitigation techniques, namely slashing and dilution. Here, we present two positive results, that both render the protocol an equilibrium and achieve maximal welfare for all parties, and a negative result, wherein an attack becomes more plausible if it severely affects the ledger's token's market price.
Bitcoin has been gaining increasing attention in academia and industry.This article investigates Bitcoin's research status and evolution via bibliometrics using a dataset of 3,873 publications between 2012 and 2022 from the Web of Science Core Collection.The findings reveal a significant increase in research on Bitcoin since 2017, coinciding with the cryptocurrency bull market.The article identifies publication trends, influential contributors, collaboration networks, and topics evolution in Bitcoin research.The three Bitcoin research stages are conceptualisation and fundamentals of Bitcoin (2012-2016), cryptocurrency and market efficiency (2017)(2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019-2022).The four prominent emerging areas for future Bitcoin research are decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy.The article offers valuable insights for researchers, policymakers, and practitioners, facilitating a better understanding of the status quo of Bitcoin research.
Elohim Fonseca dos Reis, Alexander Teytelboym, Abeer ElBahrawy, Ignacio De Loizaga · 5 authors
Dark web marketplaces have been a significant outlet for illicit trade, serving millions of users worldwide for over a decade. However, not all users are the same. This paper aims to identify the key players in Bitcoin transaction networks linked to dark markets and assess their role by analysing a dataset of 40 million Bitcoin transactions involving the 31 major markets in the period 2011-2021. First, we propose an algorithm that categorizes users either as buyers or sellers, and show that a large fraction of the trading volume is concentrated in a small group of elite market participants. We find that the dominance of markets is reflected in trading properties of buyers and sellers. Then, we investigate both market star-graphs and user-to-user networks, and highlight the importance of a new class of users, namely 'multihomers', who operate on multiple marketplaces concurrently. Specifically, we show how the networks of multihomers and seller-to-seller interactions can shed light on the resilience of the dark market ecosystem against external shocks. Our findings suggest that understanding the behavior of key players in dark web marketplaces is critical to effectively disrupting illegal activities.
With the ever-increasing advancement in blockchain technology, security is a significant concern when substantial investments are involved. This paper explores known smart contract exploits used in previous and current years. The purpose of this research is to provide a point of reference for users interacting with blockchain technology or smart contract developers. The primary research gathered in this paper analyses unique smart contracts deployed on a blockchain by investigating the Solidity code involved and the transactions on the ledger linked to these contracts. A disparity was found in the techniques used in 2021 compared to 2023 after Ethereum moved from a Proof-of-Work blockchain to a Proof-of-Stake one, demonstrating that with the advancement in blockchain technology, there is also a corresponding advancement in the level of effort bad actors exert to steal funds from users. The research concludes that as users become more wary of malicious smart contracts, bad actors continue to develop more sophisticated techniques to defraud users. It is recommended that even though this paper outlines many of the currently used techniques by bad actors, users who continue to interact with smart contracts should consistently stay up to date with emerging exploitations.
Cryptocurrency, a form of digital currency, has emerged as a disruptive force in the financial landscape. Built on the the historical context and key milestones in the development of cryptocurrencies, highlighting the release of Bit coin as the pioneering cryptocurrency. It explores the underlying technology of block chain, elucidating its decentralized nature and cryptographic security features that enable trust and accountability in transactions. Overall, this abstract offers foundations of block chain technology, cryptocurrencies offer decentralized, secure, and transparent transactions, challenging the traditional centralized financial systems. This abstract presents a comprehensive analysis of the evolution, functioning, and implications of cryptocurrencies. The study begins by examining a comprehensive overview of cryptocurrencies, providing insights into their technological foundations, economic implications, and potential future developments. It aims to contribute to the understanding of this transformative digital currency revolution and its impact on global finance and economics
Cryptoaltruism refers to the ways in which distributed ledger technologies, especially blockchains, are changing the nature of the nonprofit sector. This study specifically investigates how the blockchain technology has been used by Ukrainian nonprofits during the current Russia-Ukraine War. To link this to the more general literature on blockchains, we consider whether blockchains are used primarily as a general-purpose technology or as an institutional technology which redefines how nonprofits coordinate activities. Our analysis of Ukrainian nonprofits provides evidence supporting both perspectives. Widespread acceptance of cryptocurrency suggests blockchains are an efficiency-enhancing new technology. We also show that novel applications on nonfungible tokens to preserve art and culture and to raise funds, as well as uses of blockchains to address challenges with trust, lend support to the idea of blockchains as an innovative institutional technology that is transforming the nature of the nonprofit sector. This study intends to motivate further development of the emergent agenda on cryptoaltruism and its role in the nonprofit sector.
Abstract Since its inception in 2009, Bitcoin has become and is currently the most successful and widely used cryptocurrency. It introduced blockchain technology, which allows transactions that transfer funds between users to take place online, in an immutable manner. No real-world identities are needed or stored in the blockchain. At the same time, all transactions are publicly available and auditable, making Bitcoin a pseudo-anonymous ledger of transactions. The volume of transactions that are broadcast on a daily basis is considerably large. We propose a set of features that can be extracted from transaction data. Using this, we apply a data processing pipeline to ultimately cluster transactions via a k-means clustering algorithm, according to the transaction properties. Finally, according to these properties, we are able to characterize these clusters and the transactions they include. Our work mainly differentiates from previous studies in that it applies an unsupervised learning method to cluster transactions instead of addresses. Using the novel features we introduce, our work classifies transactions in multiple clusters, while previous studies only attempt binary classification. Results indicate that most transactions fall into a cluster that can be described as common user transactions. Other clusters include transactions made by online exchanges and lending services, those relating to mining activities as well as smaller clusters, one of which contains possibly illicit or fraudulent transactions. We evaluated our results against an online database of addresses that belong to known actors, such as online exchanges, and found that our results generally agree with them, which enhances the validity of our methods.
The evolving landscape of Decentralized Finance (DeFi) has raised critical security concerns, especially pertaining to Protocols for Loanable Funds (PLFs) and their dependency on price oracles, which are susceptible to manipulation. The emergence of flash loans has further amplified these risks, enabling increasingly complex oracle manipulation attacks that can lead to significant financial losses. Responding to this threat, we first dissect the attack mechanism by formalizing the standard operational and adversary models for PLFs. Based on our analysis, we propose SecPLF, a robust and practical solution designed to counteract oracle manipulation attacks efficiently. SecPLF operates by tracking a price state for each crypto-asset, including the recent price and the timestamp of its last update. By imposing price constraints on the price oracle usage, SecPLF ensures a PLF only engages a price oracle if the last recorded price falls within a defined threshold, thereby negating the profitability of potential attacks. Our evaluation based on historical market data confirms SecPLF's efficacy in providing high-confidence prevention against arbitrage attacks that arise due to minor price differences. SecPLF delivers proactive protection against oracle manipulation attacks, offering ease of implementation, oracle-agnostic property, and resource and cost efficiency.
Abstract Identifying illicit behavior in the Bitcoin network is a well‐explored topic. The methods proposed over time have generated great insights into the deanonymization of the Bitcoin user base through the clustering of inputs and outputs. With advanced techniques being deployed by Bitcoin users, these heuristics are now being challenged in their ability to aid in the detection of illicit activity. In this paper, we provide a comprehensive list of methods deployed by malicious actors on the network and illicit transaction mining methods. We detail the evolution of the heuristics that are used to deanonymize Bitcoin transactions. We highlight the issues associated with conducting law enforcement investigations and propose recommendations for the research community to address these issues. Our recommendations include the release of public data by exchanges to allow researchers and law enforcement to further protect the network from malicious users. We recommend the enhancement of current heuristics through machine learning methods and discuss how researchers can take the fight head‐on against expert cybercriminals.
The exploitation of smart contract vulnerabilities in Decentralized Finance (DeFi) has resulted in financial losses exceeding 3 billion US dollars. Existing defense mechanisms primarily focus on detecting and reacting to adversarial transactions executed by attackers that target victim contracts. However, with the emergence of private transaction pools where transactions are sent directly to miners without first appearing in public mempools, current detection tools face significant challenges in identifying attack activities effectively. Based on the fact that most attack logic rely on deploying intermediate smart contracts as supporting components to the exploitation of victim contracts, novel detection methods have been proposed that focus on identifying these adversarial contracts instead of adversarial transactions. However, previous state-of-the-art approaches in this direction have failed to produce results satisfactory enough for real-world deployment. In this paper, we propose LookAhead, a new framework for detecting DeFi attacks via unveiling adversarial contracts. LookAhead leverages common attack patterns, code semantics and intrinsic characteristics found in adversarial smart contracts to train Machine Learning (ML)-based classifiers that can effectively distinguish adversarial contracts from benign ones and make timely predictions of different types of potential attacks. Experiments on our labeled datasets show that LookAhead achieves an F1-score as high as 0.8966, which represents an improvement of over 44.4% compared to the previous state-of-the-art solution, with a False Positive Rate (FPR) at only 0.16%.
There is a distinct lack of criminological research examining victimisation experiences in emerging cryptocurrency frauds. At the same time, online cryptocurrency communities have become a key part of the social milieu of the cryptocurrency ecosystem where scams are commonplace. Using Reddit forum data from the subreddit r/ CryptoCurrency, this exploratory qualitative study investigates how users in an online cryptocurrency community share knowledge and experiences of cryptocurrency scams. Thematic analysis revealed how online cryptocurrency communities discuss scams by (1) arming the community (e.g. newcomer guides, personal disclosures of scam victimisation, and reflections on the technological affordances in scams); and (2) establishing community norms in response to cryptocurrency scams (e.g. protecting the community, ‘scambaiting’ practices, normalising scams as an outcome of ‘decentralisation’). Gaining a deeper understanding of cryptocurrency scam experiences provides timely insights into the intersections between victims/offenders in digital environments, how we can respond to the recent growth in cryptocurrency scams, and the variegated ways that victims seek assistance following experiences.
Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, to solve the limitations of current deep learning-based vulnerability detection methods in extracting various code critical features, using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-Scale Encoder Vulnerability Detection (MEVD) approach to hit well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model’s attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%.
Deflation represents an increase in consumer wealth through postponed consumption decisions. State-imposed "monetary policies" not only expropriate this increase in wealth attributed to deflation (a fiscal motive) but also penalize consumers for postponing their consumption of goods and services (a form of social engineering). Consequently, there is a shortfall in the state's legal frameworks adequately protecting consumer freedom and property rights. In response, the Bitcoin network has emerged as a private currency governed by a distinct legal framework rooted in proof-of-work. Consumers holding Bitcoin benefit from the economic advantages of global deflation-advantages often usurped by central banks-and experience enhanced freedom to delay consumption, navigating their life paths free from the constraints of social engineering. Thus, Bitcoin contributes value by addressing the shortfall in state legal systems that safeguard consumer freedom and property rights
Cryptocurrency tracker is an online platform that provides a userfriendly experience. Users get a simple and userfriendly experien ce through the user interface. Users can sign into their account with Gmail or a mobile number for easy access to their account. U sers can track prices of different cryptocurrencies and view currency charts. Using this user interface, users can find prices and ot her relevant information about cryptocurrencies. The app helps users to create watchlists and we can track prices. We can set alerts for cryptocurrency prices. We can customize notifications and help understand new cryptocurrency trends. Users can easily find various cryptocurrencies and track future crypt currency trends. It helps users invest in new popular cryptocurrencies that will be more useful to them in the future. Overall, the Cryptocurrency Tracker web app is a valuable tool for anyone looking to invest, trade, or just keep an eye on the cryptocurrency market. It provides realtime data and insights that can help users make informed investment decisions and stay abrea st of the latest industry trends and developments.
The tendency to transform individual social communities into a universal, unified society which knows no boundaries (or at least does not insist on them), with the necessary dose of simplification, is the conceptual definition of globalization. Since it is a tendency, globalization naturally has its own temporal dimension. The process of determining the time coordinates of this phenomenon is extremely complex and the results may vary depending on the scientific approach and point of view. However, we believe that it is possible to reach a consensus that the process of globalization is not continuous, but has several phases. We tend to think that there are three main phases of globalization. Of course, we fully respect other competing systems of periodization and argumentation on which they are based. Nevertheless, we do not doubt that proponents of different periodization would agree that a new phase of globalization began recently, although it is difficult to determine the exact moment when it was initiated. Namely, on October 31, 2009, a mysterious developer, or more likely group of developers, behind the fictious name of Satoshi Nakamoto, published the so-called White Paper titled Bitcoin: A Peer-to-Peer Electronic Cash System. More than ten years later, on March 11, 2020, the World Health Organization declared the outbreak of SARS-CoV-2 pandemic. These two, at first glance unrelated events cumulatively initiated chain of new transformations, all leading toward a more unified society. Probably the most important change, one that is the focus of this paper, is the transformation of centralized localized monetary systems into completely decentralized, digitalized, totally independent, almost entirely self-sustaining, and self-regulating global financial structure. This paper presents a part of the results collected through the theoretical-empirical research conducted previously on both legal and socio-economic moments that initiated a new phase of globalization. These results refer mostly to events that initiated a new phase of globalization, their connections, and problems of periodization. The remaining results, results on the scope and expected overall effects of this phase, will be presented in the next article in this series. All the results of the research and conclusions were collected based on different analytical-synthetical methods, mostly abstraction and generalization. In this paper specifically, different techniques of the normative method were used. Also, special techniques of legal norm interpretation were used in the process.