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This chapter explores collective information processing among black-hat hackers during their crises events. The chapter presents a preliminary study on one of Tor-based darknet market forums, during the shutdowns of two cryptomarkets. Content and network analysis of forum conversations showed that black-hat users mostly engaged with rational information processing and were adept at reaching collective solutions by sharing security advices, new market information, and alternative routes for economic activities. At the same time, the study also found that anti-social and distrustful interactions were aggravated during the marketplace shutdowns. Communication network analysis showed that not all members were affected by the crisis events, alluding to a fragmented network structure of black-hat markets. The chapter concludes that, while darknet forums may constitute resilient, solution-oriented users, market crises potentially make the community vulnerable by engendering internal distrust.

The purpose of the study is to show that divergent perceptions among regulators, the regulated and the associated regulatory bodies across multiple jurisdictions regarding the nature and functionality of cryptocurrencies hamper the development of a more comprehensive and coherent regulatory framework in curbing crimes and other related risks associated with cryptocurrencies.

The study has used a descriptive doctrinal legal research method to investigate and understand the insights of existing laws and regulations in four selected jurisdictions concerning cryptocurrencies and how these laws could be further improved and developed to reduce crypto-related crimes. Furthermore, the study has also used a comparative research method to conceptualize the contours of the new legal discourse emerging from cryptocurrencies to adopt and implement a sound regulatory framework.

The study illustrated that divergent regulatory treatment among different jurisdictions might suffocate novel digital innovations such as cryptocurrency. These fragmented regulatory approaches by various jurisdictions question the sustainability of the present national legislation adopted to regulate cryptocurrencies. Looking into other jurisdictional developments in regulating cryptocurrencies, it is apparent that a concerted regulatory approach is needed to minimize the abuse of this innovation.

The study has implications for regulators and policymakers to review the current regulatory framework for regulating cryptocurrencies to prevent regulatory arbitrage. The divergent legislative measures concerning cryptocurrency among different jurisdictions question the sustainability of these legislative initiatives, considering the evolving and borderless nature of cryptocurrency. Therefore, this paper will help regulators to consider the present legislative gaps in establishing a common global regulatory approach in the crypto sphere.

The study contributes to the existing body of literature by examining the regulatory frameworks of four jurisdictions, namely, the USA, Canada, China and the EU, related to cryptocurrencies, with a discussion on the development of cryptocurrencies-related laws among these four jurisdictions and their sustainability in curbing crimes in the Darknet.

The purpose of this paper is to assess the relevance of cryptomarkets, characteristics of purchasers and possibilities for survey research by approaching users directly on cryptomarkets.

Cross-country comparison of the results from the European Web Survey on Drugs (EWSD) and summarizing lessons learned during the data collection was carried out.

Purchasers of drugs on cryptomarkets are still a rather small segment of all drug purchasers, and most people who use cryptomarkets also use other sources of supply to buy drugs. The percentage of people using cryptomarkets is unevenly distributed across countries and substances. Purchasers on cryptomarkets in most countries are more likely to be men and more likely, on average, to use more substances. Other characteristics such as age or place of residence do not show a consistent pattern across countries. Recruitment of respondents on cryptomarkets calls for specific techniques and procedures. Specific attention should be paid to build trust and guarantee credibility and anonymity.

Interpretation of the quantitative results is limited by nonprobabilistic sampling and different recruitment strategies in different countries.

Users of cryptomarkets show some specific characteristics, providing a challenge for research and prevention agencies to keep up with digital technology. Increasing knowledge about characteristics of users of cryptomarkets may help to create adequate responses for harm reduction measures in different supply settings. However, collecting self-reported data from users on cryptomarkets is limited owing to significant privacy concerns and requires specific skills and strategies.

The EWSD provides a rare opportunity for detailed analyses of consumption patterns and characteristics of active drug users across several European countries. Furthermore, experiences of a new recruitment strategy are discussed.

The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

This chapter explores the disruptive potential of the Internet to transform illicit drug markets while also challenging stereotypical depictions and superficial understandings of supply and demand. It argues that the digital transformation of illicit drug markets combines, on one hand, a reconfiguration of the scope and impact of how sellers, buyers, and other actors interact within and upon digitally mediated retail drug markets and, on the other hand, continuing trends in the embeddedness of market structures in cultural, economic, political, and legal realms. We develop conceptual ideas for studying the architecture of digital drug markets by drawing on interdisciplinary approaches to digitalisation, markets, and drugs. To understand the functioning of online drug markets, we first need to understand digitalisation. Thus, we draw on scholarship on the digital transformation of society and, second, put forward an understanding of markets that considers how personal relations and social structures enhance and restrict market exchange. Thus, we draw on economic sociology. Third, we build on and extend social science research on illicit drug markets which points out that drug markets exhibit significant variations over time and across jurisdictions. The introduction aims to provide a research agenda that can help us to explore ongoing digital transformations of illicit drug markets. It expands and deepens scholarship on the technological, structural, economic, and cultural factors underlying the resilience and growth of digital drug markets. It also goes beyond a concern with just one type of digital drug market into wider forms of digital environments.

Drug sales facilitated through digital communication on the surface web and on darknet cryptomarkets have increased during the past two decades. This has resulted in an increase in drug law enforcement efforts to combat these markets and a subsequent increase in judicial sentencing of people selling drugs online. The aim of this study was to analyze how Swedish courts describe sentenced sellers and how the courts apply case law.

The empirical material consists of 71 sentencing documents produced by Swedish courts in cases of online drug selling between January 1, 2010 and January 1, 2020. In total, 99 sentenced persons occur in the documents. Using a qualitative research design, the authors analyzed the material through thematic text analysis.

Overall, in their descriptions of online drug sale operations, the courts’ characterizations of the concepts of street capital and digital capital show a dichotomy. These forms of capital are situationally described as both aggravating and mitigating aspects in the application of case law, indicating that it may be fruitful to view both street and digital capital as resources used on contemporary drug markets in general.

Very little research exists into how judicial systems describe and perceive the developing phenomenon of online drug sales. Using a relatively large sample from a decade of sentencing, the authors provide an analysis of how Swedish courts view and valuate capital forms in the online drugs trade.

In the age of DarkNetMarkets proliferation, combatting money laundering has become even more complicated. Constantly evolving technologies add a new layer of difficulty to already intricated schemes of hiding the cryptocurrency’s origin. Considering the latest development of cryptocurrency- and blockchain-related use cases, this study aims to scrutinize Italian and Russian antimoney laundering regulations to understand their preparedness for a new era of laundering possibilities.

One of the most recommended ways to buy and sell cryptocurrencies for illegal drug trade on DarkNet was discovered using machine learning, i.e. natural language processing and topic modeling. This study compares how current Italian and Russian laws address this technique.

Despite differences in cryptocurrency regulation, both the Italian Republic and the Russian Federation fall behind on preventing cryptolaundering.

The main contributions of this paper: consideration of noncustodial wallet projects and nonfungible token platforms through the lens of money laundering opportunities, comparison of Italian and Russian antimoney laundering regulations related to cryptocurrency, empirical analysis of the preferred method of trading/exchanging cryptocurrency for DarkNet illegal trade using machine learning techniques and the assessment of how Italian and Russian regulations address these money laundering methods.

This chapter examines how darknet drug marketplaces operate within platform capitalism. While capitalist power relations remain underexplored in research on digital drug markets, the analysis shows that the basic foundation of cryptomarkets relies on the infrastructure of platform capitalism. The authors use the concept of platform capitalism to explore cryptomarkets in an ideology-critical way. Platforms are infrastructure for the mediation of buyers and vendors; however, they are designed to extract data on the activities of their users. Platform capitalism refers to the process by which the vast collection of user data feeds into the accumulation of capital. The authors use a dialectical method to examine the constellation of digital drug platforms by disclosing a threefold contradiction: state control and self-regulation; visibility and concealment; and legality and illegality. The analysis reveals that darknet drug platforms make a profit not only from the trade of illicit drugs and the collection of user data, but also based on the illegal status of drugs, the associated ideology, and the closed ecology of darknet platforms. Power relations in cryptomarkets thereby mimic those observed in platform capitalism in general. Finally, the authors discuss the implications of platform capitalism for online drug markets.

Cryptomarkets or darknet marketplaces host multiple ‘vendors’ selling a variety of illicit products. The most sold and sought products on such markets are illegal drugs. These markets use cryptocurrencies as a payment system and provide participants with anonymity through their location on the dark web, and in recent years they have seen continuous growth in revenue and exchange. Existing literature has provided various explanations for this growth, but in 2017 the European Monitoring Centre for Drugs and Drug Addiction and Europol concluded in their 2017 ‘Drugs and the Darknet’ report that current interpretations of trends are not sufficient. This chapter will provide an alternative explanation for this phenomenon by considering web-based drug selling and purchasing in terms of trends towards ‘Uberisation’ and ‘McDonaldisation’ and applying Bourdieu’s concept of cultural capital to the discussion of the dynamic cultures of consumption and different subcultures of the drug world.