Smart contracts have become a foundational component in blockchain ecosystems, yet their vulnerabilities continue to pose significant security risks and financial losses. Traditional vulnerability detection approaches, such as symbolic execution and rule-based static analysis, often suffer from high computational cost. Recent deep learning methods attempt to learn patterns from smart contract bytecode but typically encode the entire opcode sequence without filtering, introducing noise among opcodes. To address these limitations, this paper proposes a semantics-compressed and attention-guided (SCAG) framework for smart contract vulnerability detection. SCAG introduces an Opcode Semantic Compression (OSC) mechanism to extract a compact set of semantically significant opcodes, thereby reducing noise from redundant or irrelevant instructions. These filtered opcodes are then processed by a self-attention module to capture contextual dependencies that are critical to vulnerability identification. Experiments on the SmartBugs dataset demonstrate that SCAG achieves an F1-score of 0.88 and an AUC of 0.93, outperforming Transformer-based models by approximately 5% and 4.5%, respectively, while also reducing training and inference times by over 50% and maintaining the smallest model size among all deep learning baselines.
DeFiisrevolutionizingthefinancialworldbyprovidingopen,approval-free,peer-to-peerwaysto transact,thankstoblockchain.DeFiallowsmorefinancialopportunitiesanddifference,butitalso presentsproblemsforAMLandcompliance due tohowitisdecentralized,usespseudonymsand is available in different countries. It describes in detail the barriers faced in DeFi within the EU duetotechnologyandregulations.ItdiscusseswhytraditionallawsareoftenunsuitableforDeFi, leading to questions about regulations, regulatory boundaries and any gaps in enforcing them. StudyingspecificcasesintheEU,thearticleexploresthejourneyofAMLregulationsandpoints out some of the obstacles inside the regulatory sphere due to swift changes in decentralized technology.Movingon,ithighlightsthatitisdifficulttoenforcethelawindecentralizednetworks. The study puts forward a group of guidelines in policy, law and technology to improve AML compliance in DeFi without hindering its advancements. For example, EU regulators may align theirrulesformemberstates,createbetterframeworksforliabilityofdecentralizedmarketactors, utilizeregtechandencourageteamworkbetweenregulators,technologistsandindustrymembers. Based on the findings, rigid and uncooperative regulations will not only fail to tackle issues in DeFibutalsoslowdowninnovation.Thus,thisarticleoffersideasforfuturediscussionsandrules on safeguarding money matters in the growing world of decentralized finance.
Smart contracts, which are self-executing agreements that are used on blockchain systems like Ethereum, are vulnerable to reentrancy attacks, a serious security vulnerability. These attacks enable malicious actors to repeatedly invoke a function and potentially manipulate data or deplete funds from the contract before the initial function call ends. The underlying issue lies in the way certain smart contracts handle external function calls, allowing an attacker's malicious contract to recursively call back into the vulnerable contract's function. Reentrancy attacks have been responsible for major security breaches, most notably the infamous DAO attack in 2016, where $60 million worth of Ether was stolen from the DAO contract. Mitigating reentrancy attacks is crucial for ensuring the security and reliability of smart contracts. In this paper, we have discussed multiple reentrancy attacks along with their various techniques to detect and prevent these vulnerabilities, such as implementing mutex locks, adhering to the checks-effects-interactions pattern, utilizing static code analysis tools, and incorporating manual testing frameworks. Mitigating this significant risk is crucial to protect the integrity of blockchain applications and to promote the extensive adoption of smart contract technology.
Money laundering has long been a major issue for governments, law enforcement agencies, and financial institutions around the globe. As technology advances, so too do money laundering methods, presenting new challenges for authorities and financial entities. Organised Crime Groups (OCGs) are increasingly exploiting digital platforms, cryptocurrencies, and virtual assets to disguise illicit funds while maintaining anonymity and complicating their transactions. This article analyses the problem of cryptocurrency laundering by the OCGs and various tactics employed by the OCGs to cover their trails. This article also in-depth discusses the international instruments such as the United Nations Convention against Transnational Organised Crime and Financial Action Task Force recommendations on the prevention of cryptocurrency laundering. The special focus of this paper is on the legal framework regarding cryptocurrency laundering in the United States, European Union and Malaysia. The findings of the paper suggest that there is a regulatory framework present in these jurisdictions but their regulations are not subject specific and regulatory powers have been granted to the authorities that are not specialised and skilled to tackle the problem of combating cryptocurrency laundering by OCGs.
Andrew Isaak, Baris Istipliler, Suleika Bort, Michael Woywode
Decentralized autonomous organizations (DAOs) represent a novel organizational form enabling self-governed coordination based on blockchain technology. This study examines the prototypical Bitcoin DAO from an institutional perspective, focusing on how its core features—decentralization and autonomy—interact with the broader institutional framework in which it operates. Specifically, we study how regulative institutional environments (i.e., (il)legalization) shape the growth and development of DAOs while theorizing about the role of both petty and grand corruption (i.e., by higher-level officials) in influencing the effectiveness of these regulative institutions. Our empirical analysis focuses on the global rise of Bitcoin trading and platform establishment across 49 national contexts from 2011 to 2023. Utilizing a unique data set, we find that, although the number of Bitcoin exchange platforms in a country is positively associated with Bitcoin legalization, Bitcoin trading volume is positively associated with Bitcoin illegalization. In countries with higher levels of grand corruption, Bitcoin illegalization becomes even more strongly associated with trading. In contrast, grand corruption dampens the positive association between legalization and the number of Bitcoin exchange platforms. Further, the presence of petty corruption reduces the impact of grand corruption. Our study reveals that it is critical to distinguish between petty and grand corruption as an important factor that influences the interplay between the regulative environment and growth and development of the Bitcoin DAO and the related ecosystem of Bitcoin trading and platform founding.
Shirisha Deshpande, Janmejay Shukla, Virendra Disawal, Maajid Mohi Ud Din Malik · 6 authors
The advent of cryptocurrency has transformed the global financial landscape, offering an alternative to traditional financial systems. Bitcoin, the first decentralized cryptocurrency, emerged in 2009, followed by a proliferation of various digital currencies. These developments have posed significant challenges to governments and regulatory bodies worldwide. The opaque nature of cryptocurrency regulation, combined with the decentralized structure of these assets, has led to a complex and often inconsistent regulatory environment. Given the growing importance of cryptocurrencies there is a clear need for cohesive and efficient regulatory considerations. This chapter focuses on the asserts the lack of true transparency in existing regulations, which is caused by divergent practices between jurisdictions and incompatible legal wordings as well as rapidly developing technology. This also raise concerns that the lack of a clear regulatory stance might generate more market turbulence, slow investment and foster illegal capital flow.
This chapter provides a comprehensive overview of the cryptocurrency ecosystem. It begins by tracing the market's origins, focusing on Bitcoin's 2009 launch and its transformative impact on digital transactions. Blockchain technology is explained in detail, covering its decentralized ledger system, including blocks, chains, nodes, and consensus mechanisms like Proof of Work and Proof of Stake. The chapter offers a historical perspective on mining, from early methods to advanced techniques. The workings of cryptocurrency exchanges are discussed, comparing centralized and decentralized platforms. The chapter examines cryptocurrency price determination through supply and demand dynamics and external influences such as economic trends and technological advancements etc. Market volatility and its implications for investors are analyzed, along with global regulatory approaches and their effects on the market. The chapter concludes with insights into future trends and emerging applications, offering a thorough understanding of the cryptocurrency market and its evolution.
This article critically examines the adoption of Bitcoin as legal tender in El Salvador, contextualising it within the legacy of official dollarisation after 2001. First, we empirically assess the benefits and costs of dollarisation, finding that, despite some theoretical claims, the benefits remain questionable in hindsight, while the costs for the country were relatively low. Second, we explore Bitcoin's role as legal tender, proposing its understanding as a form of International Money and its potential in facilitating remittances. Building on this, we show that the existing dollarisation and a ‘soft adoption’ of Bitcoin contributed to a comparatively low risk and low associated costs of introducing Bitcoin as a second legal tender. Third, we situate these developments within the broader geopolitical context, where the global monetary and financial system and the hegemony of the USD (the current World Money) are increasingly being repoliticised. In this light, the adoption of Bitcoin can be seen as a trial-and-error, unsuccessful at best, attempt by the Salvadoran government to enhance its leverage, improve remittance flows, and provide a low-cost escape valve in an evolving global landscape.
Felipe Mello Fonseca, M.F.S.F. de Moura, Pedro Henrique González, Diogo Mendonça
A Blockchain é uma tecnologia inovadora aplicada em diversas áreas como finanças, gestão de registros, votação eletrônica e jogos. As transações em blockchain são frequentemente executadas por smart contracts, código considerado crítico em termos de segurança. Existem diversas ferramentas que se propõe a identificar vulnerabilidades de forma automatizada em smart contracts, contudo, conforme estudos anteriores mostram, a eficácia nesta tarefa é normalmente baixa. Desse modo, identificar vulnerabilidades de forma automatizada em smart contracts continua sendo um grande desafio. Neste estudo investigamos a evolução de duas importantes ferramentas para detecção de vulnerabilidades em smart contracts: Mythril e Slither, avaliando sua eficácia na identificação de vulnerabilidades e se evoluíram comparadas a uma versão anterior. Para isto, executamos as ferramentas com versões mais rescentes em um conjunto de 69 smart contracts previamente analisados em um estudo anterior. Os resultados foram comparados com as vulnerabilidades já classificadas e submetidos a uma validação manual para aferir sua precisão. Os experimentos demonstram que Mythril apresentou melhorias na redução de falsos positivos, enquanto Slither aprimorou a detecção de falhas relacionadas a Access Control. Contudo, as ferramentas apresentaram limitações na sua evolução para alguns tipos de vulnerabilidades. Esses achados reforçam a necessidade contínua de aprimoramento e avaliação das ferramentas de análise automatizada de segurança em smart contracts.
J. Chen, Jieli Liu, Jianlin Wu, Dan Lin · 6 authors
In recent years, blockchain technology has developed rapidly and received widespread attention. However, its pseudonymous and decentralized nature has also attracted many criminal activities. Ponzi schemes, a kind of classic financial scam, also hide their true face in smart contracts, causing massive financial losses to blockchain users. Although several methods have been proposed to detect Ponzi contracts, there are still limitations in broad applicability, semantics understanding, and adversarial robustness. In this article, we propose PonziHunter, an intelligent framework for hunting Ponzi contracts on Ethereum. To tackle the problem of broad applicability, we train a detection model that does not require expert experience based on publicly available on-chain bytecode and off-chain contract labels. To tackle the problem of semantics understanding, we employ cross-function control flows and state variable dependencies to understand the logic of Ponzi contracts. Specifically, we decompile bytecodes into higher-order representations to analyze control flows and state variable dependencies and model the information as graph data. By combining the idea of code slicing, we identify the basic blocks related to Ponzi contract recognition. To tackle the problem of adversarial robustness, we model Ponzi contract recognition as a graph classification problem based on contrastive pre-training. We propose a data augmentation method for control flow graphs (CFGs), which preserves the basic blocks related to Ponzi contract recognition as much as possible during data perturbation. Experimental results show that PonziHunter outperforms state-of-the-art tools with average improvements of at least 4.77% on real-world ground-truth data and can newly discover 85 Ponzi contracts in the wild. More importantly, PonziHunter is robust against adversarial examples and can locate the critical basic blocks for smart Ponzi detection.
Decentralized Finance (DeFi) has revolutionized financial transactions by enabling open, permissionless access to financial services. However, its lack of centralized oversight and pseudonymous architecture have also brought by fraudulent activities. This study presents a novel framework for fraud detection in DeFi that integrates graph neural networks (GNNs) with multi-agent reinforcement learning (MARL). Leveraging a directed transaction graph comprising 50,000 Ethereum addresses and over 120,000 token transfers, this paper evaluates four detection pipelines: extreme gradient-boosted decision trees (XGBoost), a GNN-only model (GCN), a standalone reinforcement learning agent (PPO), and a proposed GNN+RL hybrid model. The hybrid system combines graph-based embeddings with adversarial policy learning, where a fraudster and a detector co-evolve through a multi-agent PPO setup using PettingZoo’s ParallelEnv. Synthetic fraud strategies are generated using a GAN and projected into the GCN embedding space to simulate adaptive threats. Experimental results show that while GCNs outperform flat-feature models, the GNN+RL hybrid achieves superior balance across accuracy (84.58%), AUC (0.8176), and F1 score (0.7493), capturing both structural and behavioral fraud signals. Reward convergence curves further illustrate emergent adversarial dynamics. The proposed framework demonstrates the effectiveness of combining relational inductive biases, dynamic decision-making, and adversarial augmentation for resilient fraud detection. Future work includes extending to cross-chain analytics and enriching contextual understanding through integration with large language models.
In this chapter, we present the underlying technical principles of distributed ledger technology (DLT) and blockchain technology and outline their practical applications in FinTech. In the recent years, DLT and blockchain technologies in general and cryptocurrencies, in particular, have attracted substantial attention from both researchers and practitioners due to their unique technological features such as the lack of centralized control and high level of anonymity. Because of the disruptive nature, DLT and blockchain have led to the evolution of decentralized applications in multiple domains such as finance, health care, supply chains etc. In this chapter, we first outline basic principles and foundations underpinning the DLT and blockchain technologies. Second, we discuss several applications in the FinTech domain such as cryptocurrencies, smart contracts, risk management, corporate finance, governance, crowdfunding, and derivative markets.
Mohamed Fathi, Muhammad bin Saud Al-Shammar, Gamal Sayed Khalifa Mohamed
Purpose This study aims to contribute to comprehending the challenges faced by Saudi Arabia in tackling the financial crimes enabled by cryptocurrency, especially in decentralized environments. Design/methodology/approach Content analysis is used in this study to assess the effectiveness of legal and regulatory reforms implemented in Saudi Arabia. It analyzes existing literature, case studies and relevant legal frameworks. Findings Analyzing the Saudi judiciary system, the research shows that there is a lack of proper approaches for preventing cryptocurrency-related crimes. A lack of awareness among consumers and investors exacerbates these challenges. Practical implications The Saudi government needs to improve the current legal system against the financial crimes linked with cryptocurrency: this entails enhancing public education, enhancing police capacity, as well as enhancing cooperation between nations. Originality/value This paper fulfills an identified need for research on the legal challenges Saudi Arabia faces in addressing cryptocurrency-related financial crimes within the context of Islamic law and its regulatory framework.
This study explores the transformative impact of Donald Trump’s 2024 US presidential victory on digital currency markets and regulatory frameworks. Trump’s administration, opposing central bank digital currencies (CBDCs) and championing decentralized financial systems, signifies a stark shift from the Biden administration’s cautious exploration of CBDCs. Utilizing lexicon and rule-based sentiment analysis through the valence aware dictionary and sentiment reasoner (VADER), the research quantitatively assesses shifts in political rhetoric and public sentiment. Market dynamics, including Bitcoin’s surge to $100,000, highlight Trump’s repositioning as a proponent of decentralized finance, framing CBDCs as instruments of “government tyranny” aligned with libertarian ideals. The findings indicate Trump’s policies may position the US as a cryptocurrency innovation hub while stalling federal CBDC initiatives, with far-reaching implications for global financial systems. This analysis informs debates on regulatory priorities, financial sovereignty and the US’s geopolitical role in the digital currency race.
In an increasingly digitalized and hyperconnected financial landscape, the complexity and frequency of cyber threats have grown exponentially, exposing financial institutions to real-time risks that conventional defense mechanisms struggle to mitigate.Traditional security frameworks, often reactive and siloed, lack the speed and contextual awareness required to protect dynamic finance ecosystems driven by automated trading, open banking, and decentralized financial services.This paper explores the emerging paradigm of Integrative Analytics for Autonomous Threat Response (IAATR)-a strategic synthesis of artificial intelligence (AI), behavioral modeling, and real-time analytics to secure business processes within finance ecosystems.From a broad perspective, the integration of AI into cybersecurity presents transformative possibilities.Machine learning models trained on network telemetry, user behavior, and transaction anomalies can detect threats proactively, adapt to novel attack patterns, and initiate countermeasures with minimal human intervention.The paper discusses how autonomous systemsrooted in deep reinforcement learning and explainable AI-enhance threat triage, isolate compromised processes, and orchestrate secure workflow rerouting to minimize systemic disruption.Narrowing the focus to finance-specific applications, the paper examines use cases including algorithmic fraud detection, insider threat mitigation in payment systems, and AI-enabled compliance monitoring.Emphasis is placed on the design of feedback loops between security intelligence layers and business process management (BPM) engines, ensuring that threat responses remain aligned with regulatory standards and operational continuity.The study concludes with a discussion on governance, ethical risks, and the role of digital trust in advancing AI-secured business environments.IAATR represents not just a technological leap, but a foundational shift toward anticipatory, resilient financial security architectures.
Money laundering with Cryptocurrency in Indonesia in the use of digital currencies provides a loophole for criminals to hide the results of criminal acts through money laundering practices. This article reviews various strategies used in money laundering using crypto assets, with the aim of providing a deeper understanding of this issue. This research applies the normative study method by analyzing legal aspects based on literature as well as the latest developments related to money laundering and cryptocurrencies. Money laundering through crypto assets is carried out in order to disguise the source of illegal funds. Some of the commonly used methods include transactions over the dark network as well as the use of unlicensed mixing services. This crime has been regulated in various laws and regulations that aim to prevent and eradicate the practice of money laundering through cryptocurrencies.
Jiaxin Wang, Qian’ang Mao, Hongliang Sun, Jiaqi Yan
With the development of blockchain technology, crypto gambling has gained popularity due to its high level of anonymity. However, similar to traditional casinos, crypto casinos are controlled by a few internal Delegatees, making it impossible for them to achieve complete transparency and fairness. These delegatees are hidden among gamblers and are difficult to identify and distinguish in anonymous and large-scale blockchain transaction networks. This paper proposes an unsupervised dual-stage role identification method to adaptively identify key roles and hidden delegatees in label-sparse crypto casinos. Specifically, inspired by voting-style transaction patterns, we propose a novel voting influence metric for key node identification. This metric is based on one-dimensional structural entropy to capture global dissemination capability. Subsequently, we develop a multi-view graph neural network framework enhanced with two-dimensional global structural entropy minimization and self-supervised contrastive learning to improve the robustness and interpretability of hidden role partitioning. Experiments on real-world cases of the most mainstream blockchains-Ethereum, TRON, and Arbitrum-demonstrate that our proposed method effectively reveals distinct role compositions and collusion patterns, distinguishing between gamblers and delegatees. Our results achieve a higher match with identities confirmed by judicial authorities than existing methods, indicating the effectiveness and generalizability of our approach in enhancing security and regulation oversight.
ABSTRACT Background With the popularization and application of blockchain technology, smart contracts, as one of the underlying important technologies, have naturally attracted the attention of all parties. The vulnerabilities in smart contracts will lead to information leakage, asset theft, and other problems. Motivation Existing smart contract vulnerability detection tools mostly detect vulnerabilities through a set expert mode, relying more on professional knowledge. Traditional smart contract vulnerability detection methods based on deep learning rarely pay attention to syntactic information and semantic information at the same time, and their accuracy is low. Although the method based on graph neural network alleviates this problem to some extent, it suffers from the problem of too many nodes. Methods In this paper, we propose SNOW, an advanced method for detecting smart contract vulnerabilities, which leverages statement‐level joint feature graph and hybrid graph neural network to enhance the performance and efficacy of identifying smart contract vulnerabilities. Our proposed method consists of three parts. First, we generate a new graph representation called the Joint Feature Graph (JFG), which more effectively captures code information. Next, we introduce a hybrid graph neural network designed to extract JFG graph vectors more efficiently. Finally, we classify the graph vectors. Results We have conducted extensive experiments on two datasets and compared various existing methods. The results show that our method is superior to the current state‐of‐art method in many indexes such as accuracy and precision.
Muhammad Muzammil, Abisheka Pitumpe, Xigao Li, Amir Rahmati · 5 authors
Governments and regulatory bodies have recognized investment scams as a prevalent form of cryptocurrency fraud. These scams typically use professional-looking websites to lure unsuspecting victims with promises of unrealistically high returns. In this paper, we introduce Crimson, a distributed system designed to continuously detect cryptocurrency investment scam websites as they are created in the wild. During the first 8 months of 2024, Crimson processed approximately 6 billion domain names and classified 43,572 unique cryptocurrency investment scam websites in real-time. Beyond detection, we provide insights into the design and infrastructure of these websites that can help users recognize scam patterns and assist hosting providers in detecting and blocking such sites. Furthermore, we investigate the inclusion of our detected scam websites in block-lists used by popular web browsers and applications, finding that the vast majority of these websites were absent. On the financial side, by analyzing the transactions incoming to scammer wallets on 6.7% of the sites detected by Crimson, we observe an estimated lower bound of 2.04M USD in losses due to cryptocurrency investment scams.
Noor Ul Ain Afzal, Muhammad Kamran Abid, Muhammad Fuzail, Naeem Aslam · 5 authors
Ponzi schemes have surfaced on the Ethereum platform as blockchain technology continues to gain traction. Using smart contracts, these schemes, also referred to as smart Ponzi schemes, have caused significant financial losses and adverse effects. Byte code features, op code characteristics, account qualities, and smart contract transaction behavior are the main focus areas for current Ethereum smart Ponzi scheme detection techniques. However, these methods often do not record the behavioral features of the Ponzi scheme, resulting in high false alarm rates and poor identification accuracy. In this study, we provide the source P. Source P is a unique way of knowing intelligent Ponzi schemes on the Ethereum platform, passed by dataflow. Using the intelligent contract's source code as a function eliminates the difficulty of collecting data and extracting functions from available identification methods. In particular, we convert the code into statistical flow diagrams, apply educated models, and use code representations to create classification models for the detection of Ponzi schemes. Experimental results show that SourceP outperforms cutting-edge technology in terms of sustainability and effectiveness, achieving an F1 score of 92.4% and a recall of 90.1% in Ethereum's smart Ponzi schema detection. Ponzi, Blockchain, Source Code, Intelligent Contracts.
S.H. Anak Agung Alit Satya Prananda, Kadek Januarsa Adi Sudharma
This research examines the regulations and law enforcement efforts concerning the use of cryptocurrency as a money laundering tool in both Indonesia and the United States. Using normative legal research methods and a comparative approach, the study compares the legal frameworks of the two countries. In the United States, agencies such as FinCEN, IRS, and SEC play a critical role in enforcing laws against the use of cryptocurrencies for money laundering, with comprehensive laws and sophisticated enforcement mechanisms. Meanwhile, Indonesia relies on BAPPEBTI to oversee and regulate cryptocurrency activities. Although Indonesia’s legal framework may not be as extensive as the United States', the country has taken significant steps, such as adopting the "Travel Rule" to monitor cryptocurrency transactions. However, both countries face a common challenge: the anonymity offered by cryptocurrencies, which complicates investigations into money laundering. To address this challenge, both countries require more detailed regulations and enhanced international cooperation to effectively combat the misuse of cryptocurrencies for money laundering. The research suggests that strengthening legal measures and improving global collaboration are essential to mitigate the risks associated with cryptocurrency-based financial crimes.