Dealings on the Dark Web: An Examination of the Trust, Consumer Satisfaction, and the Efficacy of Interventions Against a Dark Web Cryptomarket

Abstract Objective. The overarching goal of this thesis is to better understand not only the network dynamics which undergird the function and operation of cryptomarkets but the nature of consumer satisfaction and trust on these platforms. More specifically, I endeavour to push the cryptomarket literature beyond its current theoretical and methodological limits by documenting the network structure of a cryptomarket, the factors which predicts for vendor trust, the efficacy of targeted strategies on the transactional network of a cryptomarket, and the dynamics which facilitate consumer satisfaction despite information asymmetry. Moreover, we also aim to test the generalizability of findings made in prior cryptomarket studies (Duxbury and Haynie, 2017; 2020; Norbutas, 2018). Methods. I realize the aims of this research by using a buyer-seller dataset from the Abraxas cryptomarket (Branwen et al., 2015). Given the differences between the topics and the research questions featured, this thesis employs a variety of methodological techniques. Chapter two uses a combination of descriptive network analysis, community detection analysis, statistical modelling, and trajectory modelling. Chapter three utilizes three text analytic strategies: descriptive text analysis, sentiment analysis, and textual feature extraction. Finally, chapter four employs sequential node deletion pursuant to six law enforcement strategies: lead k (degree centrality), eccentricity, unique items bought/sold, cumulative reputation score, total purchase price, and random targeting. Results. Social network analysis of the Abraxas cryptomarket revealed a large and diffuse network where the majority of buyers purchased from a small cohort of vendors. This theme of preferential selection of vendors on the part of buyers is repeated in other findings within this study. More generally, the Abraxas transactional network can then be viewed as set of transactional islands as opposed to a large, densely connected conglomeration of vendors and buyers. With regard buyer feedback, buyers are generally pleased with their transactions on Abraxas as long as the product arrives on time and is as advertised. In general, vendors have a relatively low bar to achieve when it comes to satisfying their customers. Based on the results of the sequential node deletion, random targeting was found to be ineffective across the five outcome measures, producing minimal and a slow disruptive effect. Finally, these strategies are based on a power law where a small percentage of deleted nodes is responsible for an outsized proportion of the disruptive impact. Conclusion. As with all applied research examining emergent phenomena, this thesis lends itself to a more refined understanding of dark web cryptomarkets. While the results and conclusions drawn from these results are not perfectly generalizable to all cryptomarkets, they should serve to inform law enforcement on the dynamics which undergird these markets. To this extent, a sombre consideration of trust, consumer satisfaction, and tactical effectiveness of interventions is a necessary step towards the development of more effective countermeasures against these illicit online marketplaces. For law enforcement to be more effective against cryptomarkets, it is advised that an evidence-based approach be taken

Operation Heron – Latent topic changes in an abusive letter series

The paper presents a two-part forensic linguistic analysis of an historic collection of abuse letters, sent to individuals in the public eye and individuals’ private homes between 2007-2009. We employ the technique of structural topic modelling (STM) to identify distinctions in the core topics of the letters, gauging the value of this relatively underused methodology in forensic linguistics. Four key topics were identified in the letters, Politics A and B, Healthcare, and Immigration, and their coherence, correlation and shifts in topic evaluated. Following the STM, a qualitative corpus linguistic analysis was undertaken, coding concordance lines according to topic, with the reliability between coders tested. This coding demonstrated that various connected statements within the same topic tend to gain or lose prevalence over time, and ultimately confirmed the consistency of content within the four topics identified through STM throughout the letter series. The discussion and conclusions to the paper reflect on the findings as well as considering the utility of these methodologies for linguistics and forensic linguistics in particular. The study demonstrates real value in revisiting a forensic linguistic dataset such as this to test and develop methodologies for the fiel

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

Understanding the difference in malicious activity between Surface Web and Dark Web

The world has seen a dramatic increase in illegal activities on the Internet. Prior research has investigated different types of cybercrime, especially in the Surface Web, which is the portion of the content on the World Wide Web that popular engines may index. At the same time, evidence suggests cybercriminals are moving their operations to the Dark Web. This portion is not indexed by conventional search engines and is accessed through network overlays such as The Onion Router network. Since the Dark Web provides anonymity, cybercriminals use this environment to avoid getting caught or blocked, which represents a significant challenge for researchers. This research project investigates the modus operandi of cybercriminals on the Surface Web and the Dark Web to understand how cybercrime unfolds in different layers of the Web. Honeypots, specialised crawlers and extraction tools are used to analyse different types of online crimes. In addition, quantitative analysis is performed to establish comparisons between the two Web environments. This thesis is comprised of three studies. The first examines the use of stolen account credentials leaked in different outlets on the Surface and Dark Web to understand how cybercriminals interact with stolen credentials in the wild. In the second study, malvertising is analysed from the user's perspective to understand whether using different technologies to access the Web could influence the probability of malware infection. In the final study, underground forums on the Surface and Dark Web are analysed to observe differences in trading patterns in both environments. Understanding how criminals operate in different Web layers is essential to developing policies and countermeasures to prevent cybercrime more efficiently

Exploring Cyberterrorism, Topic Models and Social Networks of Jihadists Dark Web Forums: A Computational Social Science Approach

This three-article dissertation focuses on cyber-related topics on terrorist groups, specifically Jihadists’ use of technology, the application of natural language processing, and social networks in analyzing text data derived from terrorists\u27 Dark Web forums. The first article explores cybercrime and cyberterrorism. As technology progresses, it facilitates new forms of behavior, including tech-related crimes known as cybercrime and cyberterrorism. In this article, I provide an analysis of the problems of cybercrime and cyberterrorism within the field of criminology by reviewing existing literature focusing on (a) the issues in defining terrorism, cybercrime, and cyberterrorism, (b) ways that cybercriminals commit a crime in cyberspace, and (c) ways that cyberterrorists attack critical infrastructure, including computer systems, data, websites, and servers. The second article is a methodological study examining the application of natural language processing computational techniques, specifically latent Dirichlet allocation (LDA) topic models and topic network analysis of text data. I demonstrate the potential of topic models by inductively analyzing large-scale textual data of Jihadist groups and supporters from three Dark Web forums to uncover underlying topics. The Dark Web forums are dedicated to Islam and the Islamic world discussions. Some members of these forums sympathize with and support terrorist organizations. Results indicate that topic modeling can be applied to analyze text data automatically; the most prevalent topic in all forums was religion. Forum members also discussed terrorism and terrorist attacks, supporting the Mujahideen fighters. A few of the discussions were related to relationships and marriages, advice, seeking help, health, food, selling electronics, and identity cards. LDA topic modeling is significant for finding topics from larger corpora such as the Dark Web forums. Implications for counterterrorism include the use of topic modeling in real-time classification and removal of online terrorist content and the monitoring of religious forums, as terrorist groups use religion to justify their goals and recruit in such forums for supporters. The third article builds on the second article, exploring the network structures of terrorist groups on the Dark Web forums. The two Dark Web forums\u27 interaction networks were created, and network properties were measured using social network analysis. A member is considered connected and interacting with other forum members when they post in the same threads forming an interaction network. Results reveal that the network structure is decentralized, sparse, and divided based on topics (religion, terrorism, current events, and relationships) and the members\u27 interests in participating in the threads. As participation in forums is an active process, users tend to select platforms most compatible with their views, forming a subgroup or community. However, some members are essential and influential in the information and resources flow within the networks. The key members frequently posted about religion, terrorism, and relationships in multiple threads. Identifying key members is significant for counterterrorism, as mapping network structures and key users are essential for removing and destabilizing terrorist networks. Taken together, this dissertation applies a computational social science approach to the analysis of cyberterrorism and the use of Dark Web forums by jihadists

The Media Life of Cryptocurrencies: From Libertarian Dreams to Institutional Control

This project’s central research question is: ‘What are the key cryptocurrency discourses that exist in the crypto space, and by whom are they created?’. This thesis focuses on the historical trajectory of the media life of cryptocurrency. Specifically, it identifies cryptocurrency discourses in international news media and explores how they are socially constructed from a Social Construction of Technology (SCOT) perspective. Utilising computational topic modelling, a text analysis of cryptocurrency articles (N=4200) published from 60 countries in international news media, identified key topics associated with cryptocurrency from 2018 to 2020. The thesis presents a theoretical STS deconstruction of how cryptocurrency has been conceptually understood by media actors, accompanied by empirical evidence of the key finding that there are two major discourses which characterise news media communication about cryptocurrency: the ‘Crypto-Crime’ discourse and the ‘Financial Governance’ discourse. The main argument held in this thesis is that these two macro discourses are appropriated by international media but often emanate and are echoed from institutional positions. Vitally, this study is the first to demonstrate both theoretically and empirically, how news media in different countries ascribe diverging meaning to cryptocurrency technology, offering audiences varied images of what cryptocurrency is through discourse appropriation. Results showed that the co-constitution of discourse was strong across the UK and US whose news media appropriated the crypto-crime and crypto- governance discourses to different degrees. The thesis reveals how institutional positions are channelled through skewed news media narratives, from corporate economic and governmental control rationales. This control is demonstrated as being enacted through the state regulation of cryptocurrency, or complete bans, as in the case of China. Sometimes control is exerted through the innovation of state Central Bank Digital Currencies (CBDCs), as in many countries including the US, UK, Venezuela some EU countries. This is important because a new monetary form of digital currency can transform state macro-economic and micro-economic structures, affecting the social, economic, and political lives of global citizens

Recent Changes in Drug Abuse Scenario: The Novel Psychoactive Substances (NPS) Phenomenon

copyright 2019 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.Final Published versio

Cryptonetworks - The incentive-based Economics of Blockchain

Blockchain technology has the novel ability to ‘create' trust in a decentralised environment. With this technology, third-parties and middlemen are no longer necessary to enforce transactions. Instead, blockchain uses decentralised consensus protocols and embedded logic to enforce contracts. The applications of blockchain are vast and include cryptonetworks, the culmination of blockchain and crypto tokens. Cryptonetworks can have an impact on the business models of firms, both in terms of cost structure and value creation. By blending the functionality of centralised platforms with the community-orientated nature of the original open protocols of the internet, cryptonetworks enable value creation to be correctly assigned to the actual content creators through tokens. The work of Ronald Coase illustrated the need for firms to overcome the transaction costs of operating within the market. Cryptonetworks, however, provide an alternative ‘middle ground' option to the firm and the market, allowing both to benefit from reduced transaction costs and incentive maximisation of the market. In addition, the implementation of economics in today's cryptonetworks, often referred to as ‘cryptoeconomics', remains conventional and conservative, placing a limit on the potential of cryptonetworks. By revaluating and reconstructing today's value measurement criteria, cryptonetworks have the potential to move beyond a single ‘Hayekian price' and instead incorporate multiple other indexes that better measure and capture value creation as it pertains to wider social issues of production, distribution, and consumption of goods and services. Finally, this thesis incorporates a case study on the MakerDAO stablecoin as a practical illustration of a cryptonetwork