Follow blockchain research across journals, conferences, and preprint repositories.
1,518 results · page 10 of 64
Alessio Faccia, Asma Salman
No abstract is available for this record.
Rifa Atrous
This study aims to investigate the presence of herding behavior in the cryptocurrency market during major geopolitical crises, including the COVID-19 pandemic, the Russo-Ukrainian conflict, and the Israeli-Palestinian conflict. Relying on the CSSD and CSAD approaches, we analyze daily returns for a panel of 17 cryptocurrencies, classified as either "clean" or "dirty" based on their consensus mechanisms and associated energy consumption levels, over the period from December 30, 2019, to December 31, 2024. The results reveal that herding is not a persistent phenomenon across all market phases or crises. However, signs of convergence in investor behavior are detected during the COVID-19 period, particularly among clean cryptocurrencies. The inclusion of a sentiment index indicates that economic uncertainty has a negative and significant impact on returns, suggesting heightened risk aversion during turbulent periods. Moreover, structural break analysis confirms that clean cryptocurrencies are more sensitive to stress events, exhibiting greater behavioral shifts. These findings underscore the heterogeneous nature of investor behavior in the cryptocurrency market and the influence of crisis-induced sentiment over traditional price dynamics. This highlights the need for closer monitoring of environmentally labeled crypto assets under extreme market conditions.
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.
Chen Sun, Nurshazwani Muhamad Mahfuz, WANG YALI
No abstract is available for this record.
Oluleye M. Adewuyi
The swift rise of cryptocurrencies has created both groundbreaking opportunities and unique challenges regarding financial crime. The decentralized and pseudo-anonymous characteristics of these digital currencies can facilitate illegal activities; however, the transparency inherent in blockchain technology also provides groundbreaking methods for detection and prevention. This paper investigates the connection between cryptocurrencies and financial crime by outlining common types of crypto-related offenses. Additionally, it explores the growing field of blockchain forensics and assesses the effectiveness of blockchain analytics tools in reducing cryptocurrency theft and bolstering law enforcement efforts. Through a critical analysis of the strengths and weaknesses of blockchain technology, this paper aims to deepen understanding of the changing dynamics of financial crime in the digital era, offering insights for researchers, policymakers, and practitioners interested in leveraging the transparency of blockchain for crime prevention.
Nicolin Decker
No abstract is available for this record.
Joseph P. Helou, Aya Farhat
No abstract is available for this record.
David Krause
No abstract is available for this record.
Daewoong Cho, Gowri Ramachandran, Raja Jurdak, Salil S. Kanhere
No abstract is available for this record.
Olga N. Volkova
Cryptocurrencies are a type of financial instrument that has been widely used by financial market participants since the early 2010s. Despite their growing popularity, their status within financial systems across different countries remains a topic of ongoing discussion. There is still no consensus on how to best understand the economic nature of these digital assets. This paper uses discourse analysis and content analysis to explore the various interpretations of cryptocurrencies’ economic nature. The paper argues that the interpretation of cryptocurrency’s economic nature depends heavily on the perspective of the stakeholder and the intended purpose of using the term. It considers arguments both for and against treating cryptocurrencies as commodities, currencies (including electronic and private currencies), or properties (assets, such as financial assets). It concludes that traditional cryptocurrencies do not meet the criteria for being considered money, and only central bank-issued digital currencies can fulfill all the functions associated with money. Decentralized cryptocurrencies, such as Bitcoin, cannot be classified as securities because there are no companies or organizations that issue these assets and bear any obligations under them. Instead, these assets have the characteristics of commodities. Different types of cryptocurrencies can be treated as either commodities or securities for tax purposes, depending on the specific circumstances. At the same time, assets with unique characteristics and behavior in the financial market may be included in a separate category for accounting purposes, or if the state allows for the use of cryptocurrencies in transactions without restrictions, they can be considered equivalent to cash.
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 .
V. V. Polyakov
The article examines the problem of digital transformation within organized criminal groups. This transformation results from the introduction of innovative digital technologies into criminal activities and is manifested in the formation of decentralized organizational structures for criminal groups based on network management principles. It is shown that the transition from traditional hierarchical structures to alternative structures based on network interaction among participants is primarily typical of criminal groups specializing in high-tech crimes. In practice, the evolution of organized criminal activity has led to the emergence of groups with complex symbiotic structures, characterized by a combination of network interaction among the group's structural elements while maintaining a hierarchical structure within its governing core. The factors characterizing criminal groups with decentralized mixed structures are described, including a higher level of self-organization, increased group stability, adaptability to external conditions, responsiveness in addressing emerging issues, and enhanced anonymization of participants. The phenomenon of forming a new organizational structure for criminal groups has been revealed. This form is most accurately described by the concept of an ecosystem of criminal communities. It is shown that ecosystems of criminal communities are network associations of autonomous criminal groups interacting with one another based on the principle of "Crime as a Service", similar to the interaction principles found in business ecosystems within the digital economy. The technological basis for these ecosystems consists of digital platforms created and operating on the Darknet, which represent a shadow information environment that unites network services, data, and digital resources, facilitating the joint activities of ecosystem participants. The formation of ecosystems of criminal communities appears to be a qualitatively new and dangerous phenomenon. The criminal advantages gained from this transformation include the ease of attracting resources through the development of a global network market for criminal services and products on the Darknet; the "division of labor" through the narrow specialization of autonomous groups entering into cooperative and collaborative relationships; access to advanced means for committing high-tech crimes through the commercialization of criminal innovations; the rapid incorporation of new participants; the ease of overcoming interregional and interstate borders to commit transnational criminal acts; a combination of globalization and glocalization factors; and a significant reduction in the risk of criminal prosecution due to maximum anonymization of participants. The article concludes that the formation of ecosystems of criminal communities is becoming the dominant trend in the evolution of modern organized crime.
Mohamed Gamel Khalafallah Hemdani
No abstract is available for this record.
David Krause
No abstract is available for this record.
Romina Gabbiadini, Lorenzo Gobbi, Eugenio Rubera
No abstract is available for this record.
Stefano Ferretti, Gabriele D’Angelo, Vittorio Ghini
Cryptocurrency money laundering is a pressing issue, as it not only facilitates and hides criminal activities but also disrupts markets and the overall financial system. To respond this challenge, researchers are trying to develop robust Anti-Money Laundering (AML) frameworks. These efforts play a crucial role in promoting societal welfare by mitigating the impact of criminal activities. This paper explores the application of Graph Neural Networks (GNNs) for classifying Bitcoin transactions. The research specifically employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), the Chebyshev spatial convolutional neural networks, and GraphSAGE networks. Based on the dataset analysis, we experiment with different subsets of features. Our findings suggest that the use of Graph Neural Network convolutions, combined with a final linear layer and skip connections, allow for an improvement in the state-of-the-art results, especially when Chebyshev and GATv2 convolutions are used.
Nicola Ruaro, Fabio Gritti, Rob McLaughlin, Dongyu Meng · 7 authors
No abstract is available for this record.
Khalid Abdulla, Asma Salman
No abstract is available for this record.
Anagha Vinayak
No abstract is available for this record.
kaushal patidar
No abstract is available for this record.
Ali Shirvanporzour
No abstract is available for this record.
Helena Rong, David Dam
No abstract is available for this record.
Harry Wilson
No abstract is available for this record.
David Krause
No abstract is available for this record.