Mohammad Meezan -, Kande Vinay Karthik -, Mohammed Mehraj Pasha -, Sandhya S -
In this paper, we describe the position regulation of cryptocurrencies in India and compare it with the worldwide regulatory framework. It analyzes the influence such regulations have on the Indian market, indicating the obstacles as well as opportunities. It emphasizes the importance of focusing on such measures which both stimulate the growth of new ideas and the strength of the market as well as the security of investors. This paper aims at providing guidance on how to improve India’s approaches towards the regulation of the cryptocurrency market through a comparative perspective.
Ammad Aslam, Octavian Postolache, Sancho Oliveira, J. M. Dias Pereira
Sharding is an emerging blockchain technology that is used extensively in several fields such as finance, reputation systems, the IoT, and others because of its ability to secure and increase the number of transactions every second. In sharding-based technology, the blockchain is divided into several sub-chains, also known as shards, that enhance the network throughput. This paper aims to examine the impact of integrating sharding-based blockchain network technology in securing IoT sensors, which is further used for environmental monitoring. In this paper, the idea of integrating sharding-based blockchain technology is proposed, along with its advantages and disadvantages, by conducting a systematic literature review of studies based on sharding-based blockchain technology in recent years. Based on the research findings, sharding-based technology is beneficial in securing IoT systems by improving security, access, and transaction rates. The findings also suggest several issues, such as cross-shard transactions, synchronization issues, and the concentration of stakes. With an increased focus on showcasing the important trade-offs, this paper also offers several recommendations for further research on the implementation of blockchain network technology for securing IoT sensors with applications in environment monitoring. These valuable insights are further effective in facilitating informed decisions while integrating sharding-based technology in developing more secure and efficient decentralized networks for internet data centers (IDCs), and monitoring the environment by picking out key points of the data.
Abstract Blockchain technology has gained widespread attention and adoption in various industries. However, despite its potential benefits, there are still numerous challenges and issues that need to be addressed. This paper provides an overview of the legal, regulatory, and technical challenges related to the use of blockchain technology. It explores the challenges associated with privacy, data protection, and data security, and analyses the regulatory challenges and implications. Additionally, it identifies future challenges and issues that may arise in the field of blockchain technology, including the integration with emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data. The paper concludes by discussing the need for collaboration among stakeholders and the development of comprehensive legal and regulatory frameworks to address the challenges and ensure the successful implementation of blockchain technology in various sectors.
This research explores the impact of Non-Fungible Token (NFT) authentication on purchase intention in new and pre-loved luxury markets, grounded in warranting theory and institution-based trust theory. Using a two-study online experimental design (Study 1: new luxury market, Study 2: pre-loved luxury market), both studies used a one-factor (NFT authentication) and two-level (yes or no) design and PROCESS macro Model 6 for serial mediation analysis. The results from Study 1 indicate that NFT authentication enhances purchase intention through increased warranting value and structural assurance. Study 2 confirmed these serial mediating effects and revealed a direct significant impact of NFT authentication in the pre-loved luxury market, which was not significant in the new luxury market. This study highlights the importance of NFT authentication in enhancing consumer trust and purchase intention in both new and pre-loved luxury markets.
Abstract This study investigates the dark side of the non-fungible token (NFT) marketplace, with a focus on understanding the risks, and underlying factors driving fraud in the NFT ecosystem. Using the fraud triangle framework, this study examines pressure, opportunity, and rationalization from individual and organizational perspectives. The research provides a comprehensive understanding of the contributing factors to NFT marketplace fraud by analyzing the reasons behind fraudulent actions. A conceptual framework is developed that includes ten propositions to aid in understanding the complexity of this issue. This study’s outcomes will assist policymakers in crafting efficient approaches to mitigate fraud within the NFT marketplace.
S M Mostaq Hossain, Amani Altarawneh, Jesse Roberts
As blockchain technology and smart contracts become widely adopted, securing them throughout every stage of the transaction process is essential. The concern of improved security for smart contracts is to find and detect vulnerabilities using classical Machine Learning (ML) models and fine-tuned Large Language Models (LLM). The robustness of such work rests on a labeled smart contract dataset that includes annotated vulnerabilities on which several LLMs alongside various traditional machine learning algorithms such as DistilBERT model is trained and tested. We train and test machine learning algorithms to classify smart contract codes according to vulnerability types in order to compare model performance. Having fine-tuned the LLMs specifically for smart contract code classification should help in getting better results when detecting several types of well-known vulnerabilities, such as Reentrancy, Integer Overflow, Timestamp Dependency and Dangerous Delegatecall. From our initial experimental results, it can be seen that our fine-tuned LLM surpasses the accuracy of any other model by achieving an accuracy of over 90%, and this advances the existing vulnerability detection benchmarks. Such performance provides a great deal of evidence for LLMs' ability to describe the subtle patterns in the code that traditional ML models could miss. Thus, we compared each of the ML and LLM models to give a good overview of each model's strengths, from which we can choose the most effective one for real-world applications in smart contract security. Our research combines machine learning and large language models to provide a rich and interpretable framework for detecting different smart contract vulnerabilities, which lays a foundation for a more secure blockchain ecosystem.
Cryptocurrencies are volatile digital currencies based on a decentralized system. Their market behavior, shaped primarily by communal factors such as developer activity and community engagement, differs from that of traditional financial instruments, which are typically driven by intrinsic factors. This study examines the impact of community engagement, as measured by developer activity on GitHub, on the valuation and trading volume of decentralized assets. A quantitative research design is used to analyze developer data from multiple cryptocurrencies. Statistical methods, including correlation analysis, are applied to assess the strength of the relationships between developer activity, asset valuation, and trading volume. Preliminary findings indicate a consistent correlation between developer engagement and both asset valuation and trading volume, offering insight into what drives the success of cryptocurrency projects. This research contributes to the rapidly growing field of cryptocurrency market analytics, highlighting developer activity as a predictive indicator and a potential tool for anticipating shifts in both market dynamics and community sentiment.
Blockchain technology, characterized by features such as decentralization, is transforming the financial system and providing new tools for financial crime governance. However, its characteristics like anonymity are also exploited by criminals, giving rise to new types of financial crime. This paper analyzes its "double-edged sword" effect from a financial professional perspective: first, it outlines the technical principles and current applications; then, it explores its empowering mechanisms as a "sharp sword" in anti-money laundering, combating terrorist financing, and enhancing transaction transparency. Subsequently, it analyzes its abuse as a "dark blade" in criminal activities such as cryptocurrency money laundering. Employing the financial regulation "trilemma" framework, the paper argues for the necessity and challenges of seeking a balance between decentralization, privacy protection, and effective regulation. It proposes comprehensive governance pathways, including building an adaptive regulatory framework that synergizes "RegTech" and "Compliance Tech." The research indicates that guiding blockchain technology to serve financial security and stability requires acknowledging and mastering its dual nature.
As a distributed shared transaction ledger, blockchain technology has the characteristics of decentralization, immutable, irreversible and traceable, and is changing the inherent model of traditional industries. Smart contracts, as one of the core applications of blockchain technology, provide the basis for a variety of practical applications. However, the frequent security problems of smart contracts have not only caused huge economic losses, but also hindered the development of blockchain systems. According to relevant studies, the economic losses caused by smart contract security breaches have exceeded billions of dollars. Therefore, the security of smart contracts has become a hot topic at home and abroad. This study discusses the technical vulnerabilities and corresponding solutions of smart contracts from the code level. In-depth analysis of security vulnerabilities in smart contracts can help identify and repair potential security hazards, improve the overall security of contracts, and prevent the theft of funds or abnormal execution of contracts. By raising the security awareness of developers, enterprises and users, it is possible to promote the application of smart contracts in high-security fields such as finance and law and promote the healthy development of the blockchain ecosystem.
In the era of deep integration between the digital economy and globalization, virtual currencies represented by Bitcoin, with their decentralized architecture, anonymous transaction characteristics, and crossborder circulation advantages, have become a new carrier for cross-border money laundering crimes. Statistics show that the global virtual currency money laundering scale exceeded the $20 billion threshold in 2024, with cross-border money laundering accounting for 60%. The cross-regional mobility, technological concealment, and regulatory arbitrage characteristics of such crimes pose a subversive challenge to the traditional anti-money laundering governance system. Through in-depth deconstruction of the four core models of virtual currency money laundering—anonymous wallet mixing services, cross-chain bridging and decentralized finance (DeFi) operations, and darknet trading ecosystems—it is evident that they face governance dilemmas in electronic data forensics, such as massive and decentralized data storage and enhanced anonymity technology countermeasures. In response to the new patterns of money laundering crimes in the big data era, public security and judicial authorities need to break down industry barriers, establish cross-departmental judicial collaboration mechanisms, and promote the construction of cloud-based think tank systems. These measures will significantly improve the efficiency and accuracy of electronic data forensics, providing a solid judicial guarantee and technical support for combating cross-border virtual currency money laundering crimes and safeguarding national financial security and order.
Alexander Brechlin, Jochen Schäfer, Frederik Armknecht
ABSTRACT Cryptocurrency exchanges have become a multi‐billion dollar industry. Although these platforms are not only relevant for economic reasons but also from a privacy and legal perspective, empirical studies investigating the operations of cryptocurrency exchanges and the behavior of their users are surprisingly rare. A notable exception is a study analyzing the cryptocurrency exchange ShapeShift . While this study described new heuristics to retrieve a significant fraction of trades made on the plaform, its approach relied on identifying cryptocurrency transactions based on previously scraped trade data. This limited the analysis to the timeframe for which data had been acquired and likely led to false negatives in the transaction identification process. In this paper, we replicate and extend previous work by conducting an in‐depth investigation of the cryptocurrency exchange Evonax . Our analysis is based on actual trading data acquired by using a novel methodology allowing to extract detailed information from the public blockchain and the interface of the exchange platform. We are able to identify 30,402 transactions between the launch of Evonax in February 2018 and December 31, 2022, which should be close to a complete set of all transactions. This allows us not only to analyze the business practices of a cryptocurrency exchange but also to identify a number of interesting use cases that are likely to be associated with illegal activity. This paper is an extended version of a research article previously accepted at the CryptoEx Workshop at IEEE ICBC 2024.
Σκοπός της παρούσας διπλωματικής εργασίας ήταν η διερεύνηση του φαινομένου της νομιμοποίησης εσόδων από εγκληματικές δραστηριότητες μέσω της χρήσης κρυπτοστοιχείων. Αρχικά, ορίστηκαν και κατηγοριοποιήθηκαν τα κρυπτοστοιχεία και αναλύθηκε ο τρόπος λειτουργίας τους με τη χρήση της τεχνολογίας του κατανεμημένου καθολικού. Στη συνέχεια, μέσα από την ανασκόπηση της εξέλιξης του φαινομένου της νομιμοποίησης παράνομων εσόδων, φτάνοντας στους σύγχρονους τρόπους εφαρμογής του, αναδείχθηκαν τα πλεονεκτήματα που παρέχει η χρήση κρυπτοστοιχείων στο πλαίσιο εγκληματικών πρακτικών νομιμοποίησης. Ακολούθως, αναλύθηκαν, συστηματικά οι κύριες μέθοδοι που χρησιμοποιούνται για τη νομιμοποίηση παράνομων εσόδων μέσω κρυπτοστοιχείων. Παρουσιάστηκαν πρακτικές που αξιοποιούν τόσο κεντρικά όσο και αποκεντρωμένα συστήματα, από μη αδειοδοτημένα κεντρικά έως αποκεντρωμένα ανταλλακτήρια, πλατφόρμες P2P και OTC μεσίτες, σε συνδυασμό με υπηρεσίες ανάμειξης, παρένθετα πρόσωπα (“money mules”) και τη μέθοδο μεταπήδησης σε άλλο blockchain (“chain hopping”). Ειδική αναφορά έγινε στα πορτοφόλια ιδιωτικότητας (privacy wallets), στα ιδιωτικά νομίσματα (privacy coins), στα σταθερά κρυπτονομίσματα (stablecoins) και στις μη εναλλάξιμες μάρκες (non fungible tokens/NFTs). Στο ίδιο πλαίσιο, επεξηγήθηκε η αξιοποίηση των αυτόματων μηχανημάτων ανάληψης κρυπτοστοιχείων (crypto-ATMs) και προπληρωμένων καρτών, τα οποία παρέχουν τη δυνατότητα ταχείας μετατροπής κρυπτοστοιχείων σε παραστατικά νομίσματα και αντιστρόφως. Παρουσιάστηκαν, επίσης, νεότερες, αναδυόμενες τεχνικές, ενδεικτικές της συνεχούς προσαρμογής των εγκληματικών μεθόδων στις τεχνολογικές εξελίξεις. Εξετάστηκε το υφιστάμενο νομοθετικό πλαίσιο, τόσο σε ευρωπαϊκό όσο και σε εθνικό επίπεδο, και αναλύθηκαν οι πρόσφατες κανονιστικές εξελίξεις, ήτοι ο Κανονισμός MiCA (ΕΕ 2023/1114) και η ευρωπαϊκή δέσμη νομοθετημάτων, γνωστή ως “AML Package”. Στο εθνικό επίπεδο παρουσιάστηκε ο Ν. 5193/2025, ο οποίος θεσπίστηκε με στόχο τη λήψη των αναγκαίων εθνικών μέτρων για την ορθή και ενιαία εφαρμογή των ανωτέρω ευρωπαϊκών πράξεων. Η εργασία κατέδειξε ότι, παρά τον επαναστατικό χαρακτήρα των κρυπτοστοιχείων ως τεχνολογικού εργαλείου και τις πολλαπλές εφαρμογές τους, αυτά δύνανται ταυτόχρονα να χρησιμοποιηθούν ως μέσο νομιμοποίησης παράνομων εσόδων. Η ταχύτητα της τεχνολογικής προόδου, σε συνδυασμό με την καθυστέρηση προσαρμογής του νομοθετικού πλαισίου, αποτελούν κρίσιμες σύγχρονες προκλήσεις για την αποτελεσματική αντιμετώπιση του φαινομένου.
Cryptocurrency exchange hacks remain a persistent threat, posing significant financial and security risks.The 2025 Bybit hack, resulting in approximately $1.4 billion in losses, is the largest cryptocurrency heist to date, highlighting the vulnerabilities even among leading exchanges.This paper examines the implications of such breaches on market stability, regulatory policies, and investor confidence, particularly within the context of the Trump administration's deregulatory approach to digital assets.The analysis explores the trade-offs between promoting innovation and ensuring robust security frameworks, emphasizing the potential for policy adjustments in light of escalating cyber threats.Additionally, the study reviews historical exchange hacks, demonstrating a pattern of increasing sophistication among malicious actors.The findings suggest that regulatory clarity and enhanced security measures are essential for the long-term stability of the cryptocurrency ecosystem.Future research directions include evaluating global regulatory responses, the role of decentralized exchanges, and the effectiveness of cybersecurity protocols.
Terrence August, Duy Dao, Kihoon Kim, Marius Florin Niculescu
Cryptocurrencies have prompted a shift away from classic security attacks toward ransomware-based extortion. To better understand the impact of cryptocurrencies on the cybersecurity landscape, we conduct a comparative analysis of cybersecurity metrics prior to and after the adoption of cryptocurrency using a series of connected software-use models in the presence of security externalities. In this framework, we endogenize the actions of both heterogeneous consumers and attackers, with entry of the latter being driven by both the size of the unpatched consumer population and, as a subset of it, the size of the ransom-paying consumer population. We first examine users’ adoption and patching behavior under both security scenarios. We explore how changes in attacker entry costs impact outcomes under both conventional and post-crypto ransomware threat landscapes. We show that ransomware scenarios may be more desirable than conventional ones when attacker entry costs are low, provided that the gains from entering with standard attacks under the ransomware scenario are not too high. However, under such scenarios, social welfare can increase under the same conditions that lead to larger ransoms being demanded and a higher expected total ransom being paid, which presents a conundrum to policymakers. We also examine the impact of market parameters associated with security losses from conventional attacks and residual losses when victims pay in ransomware attacks. This paper was accepted by Kay Giesecke, finance. Funding: This work was partially supported by Insung Research Grant of KUBS, the LG Yonam Foundation (of Korea), and an award from the Georgia Institute of Technology Center of International Business Education and Research as part of its funded research program. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2023.00969 .
Bitcoin is a decentralized cryptocurrency, which is rapidly growing and offering many advantages. Although its structure protects users from some types of fraud, it is not completely immune, while fraud detection in Bitcoin remains still relatively unexplored. In this paper, we use a graph to model Bitcoin transactions and benefit from the graph’s structure to overcome the lack of informative transaction and user data. We utilize network analysis for feature extraction and model fraud detection as a classification problem using a Deep Neural Network as our classifier. Furthermore, we propose a novel approach that combines a Variational Graph Autoencoder (VGAE), for deriving appropriate node and graph embeddings, and supervised learning to detect fraudulent Bitcoin transactions. Our experimental results show that the proposed approach, while also affected by high class imbalance, similarly to using only the graph-based features for classification, performs significantly better in detecting high-risk areas in the graph.
The rapid expansion of cryptocurrencies has revolutionized the digital economy, offering decentralized and secure transaction mechanisms through blockchain technology. However, this growth has concurrently attracted cybercriminal activities, exploiting the inherent anonymity and security features of cryptocurrencies for illicit purposes such as money laundering, ransomware, exchange hacking, tax evasion, Initial Coin Offering (ICO) frauds, Ponzi schemes, and phishing attacks. This paper provides a comprehensive analysis of cryptocurrency-related cybercrimes, identifying prevalent patterns and underlying vulnerabilities within blockchain systems and cryptocurrency exchanges. Utilizing a multifaceted research methodology that includes qualitative and quantitative analyses, case studies, and theoretical frameworks like the Martial Arts Matrix (MAM), the study elucidates the motivations and sophisticated tactics employed by cybercriminals. Key findings highlight the critical need for enhanced security measures, robust regulatory frameworks, and collaborative efforts among stakeholders to mitigate these risks effectively. Additionally, the paper explores emerging trends and technologies in blockchain security, such as decentralized identity management and quantum-resistant cryptographic algorithms, which hold promise for strengthening defenses against evolving cyber threats. The study concludes by offering actionable recommendations for law enforcement agencies, cryptocurrency providers, and policymakers to address the dynamic landscape of cryptocurrency-related cybercrimes, ensuring the sustained growth and trustworthiness of the cryptocurrency ecosystem.
Blockchain's decentralized characteristics have posed unique challenges and unlocked novel opportunities for the accounting and auditing sector. While the potential impact of blockchain and smart contracts on auditing has been raised, comprehensive studies remain scarce. Using the Solidity language, this study explores the viability of encoding into smart contracts specific auditing rules that can automatically identify suspicious transactions in common fraud schemes. To illustrate the feasibility, it presents a proof-of-concept framework encompassing system architecture, smart contract development, and workflow procedures. Simulation results demonstrate that blockchain-based smart contract approach in this study can effectively identify problematic transactions in near real-time. Consequently, this could help auditors to allocate audit resources to focus efforts on higher risk transactions. The findings provide implications for future studies on the application of smart contracts in auditing.