Illicit Transactions Research Articles

Beyond the Onion Routing: Unmasking Illicit Activities on the Dark Web

This comprehensive study delves into the complexities of the Dark Web, a concealed segment of the internet that remains invisible to standard search engines and is accessible only through specialized tools like The Onion Router (TOR), which ensures user anonymity. While the Dark Web is celebrated for its capacity to safeguard privacy and foster free expression, it concurrently serves as a sanctuary for illegal endeavours, encompassing drug trafficking, unauthorized arms trading, and a spectrum of cybercrime. The primary objective of this research is to scrutinize the efficacy of onion routing, the foundational technology behind the Dark Web, in preserving user anonymity amidst escalating efforts by law enforcement agencies to dismantle illegal activities. This paper adopts a rigorous approach that melds an exhaustive review of pertinent literature with empirical investigations to pinpoint the intrinsic vulnerabilities within the onion routing framework. Furthermore, the study introduces innovative methodologies aimed at bolstering the detection and neutralization of illicit transactions and communications on the Dark Web. These proposed methods seek to establish a delicate balance between upholding the Dark Web's legitimate functions—such as protecting privacy and enabling free speech—and curtailing its misuse for criminal activities. The paper culminates in a discussion of the broader implications of these findings for policymakers, law enforcement officials, and privacy advocates. It provides a set of recommendations for future research and policy formulation in this intricate and ever-evolving domain, to navigate the challenges posed by the Dark Web while preserving its essential values.

Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis

As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating explainable artificial intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The shapley additive explanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we introduce an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances true positive rate (TPR) and receiver operating characteristic-area under curve (ROC-AUC) scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and false positive rate (FPR) scores.

Analysis of the Implementation of Islamic Values and Islamic Marketing Mix Case Study at Lor Internasional Hotel Syariah Solo

This study aims to determine whether Lor Internasional sharia hotel solo applies Islamic values and Islamic marketing mix. The application of Islamic values and the Islamic marketing mix is starting to be widely applied by Muslim business people, because Muslims are starting to realize the importance of sharia-based hotels to avoid unwanted things such as murder, drugs, illicit transactions and adultery. The approach in this research uses descriptive qualitative, data collection by observation in Islamic hotels in Solo, and conducts interviews with employees, consumers, and documentation. The results showed that Lor internation- al hotel syariah solo has implemented Islamic values in the form of tauhid values, khilafah, worship, and ihsan that have been implemented. And has fulfilled the Islamic marketing mix which includes products, prices, promotions, places, people, processes and physical evidence that have been implemented, but it has not fulfilled the requirements for the application of the Islamic marketing mix at Islamic prices (prices) and Islamic promotions (promotions), because of the application of Islamic prices in Islamic hotels in Solo there are still prices that are not transparent and the implementation of Islamic promotions at Islamic hotels in Solo is still misleading. Therefore, Lor international hotel sharia solo must maintain the Islamic values that have been implemented, and improve the Islamic marketing mix that has not been implemented properly in order to survive in the long term and continue to develop.

Uncovering and Mitigating the Hidden Chasm: A Study on the Text-Text Domain Gap in Euphemism Identification

Euphemisms are commonly used on social media and darknet marketplaces to evade platform regulations by masking their true meanings with innocent ones. For instance, “weed” is used instead of “marijuana” for illicit transactions. Thus, euphemism identification, i.e., mapping a given euphemism (“weed”) to its specific target word (“marijuana”), is essential for improving content moderation and combating underground markets. Existing methods employ self-supervised schemes to automatically construct labeled training datasets for euphemism identification. However, they overlook the text-text domain gap caused by the discrepancy between the constructed training data and the test data, leading to performance deterioration. In this paper, we present the text-text domain gap and explain how it forms in terms of the data distribution and the cone effect. Moreover, to bridge this gap, we introduce a feature alignment network (FA-Net), which can both align the in-domain and cross-domain features, thus mitigating the domain gap from training data to test data and improving the performance of the base models for euphemism identification. We apply this FA-Net to the base models, obtaining markedly better results, and creating a state-of-the-art model which beats the large language models.

Harmonising cryptocurrency regulation in Europe: opportunities for preventing illicit transactions

This study focuses on the analysis of cryptocurrency regulatory developments in Europe. The aim is to review national crypto-legislation in Europe and the EU's latest initiative to create designated regulatory instruments for the crypto-economy. This study assessed whether the European Union's Regulation on Markets in Crypto-Assets (MiCA) would have the intended effect. Drawing on the results of a survey of crypto experts from five European countries, this study evaluated the effectiveness of current regulation across Europe and how it can be improved to reduce financial crimes. The findings show that a unified national legal framework for regulating transactions with crypto assets does not exist in European countries. Current crypto regulations are dictated by anti-money laundering recommendations. This study provides suggestions for improving MiCA regulation. The article offers recommendations for an international regulatory standard for crypto assets and insights for increasing efficiency in regulating DeFi, NFTs, and smart contracts.

Bitcoin Money Laundering Detection via Subgraph Contrastive Learning.

The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address-transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others.

Social influence in the darknet market: The impact of product descriptions on cocaine sales

BackgroundThe rise of the darknet market, supported by technologies such as the Tor Browser and cryptocurrencies, has created a secure environment in which illicit transactions can occur. However, due to the lack of government oversight in this hidden online domain, darknet markets face significant challenges in upholding social order. Hence, this study explores the social dynamics that promote social order in a darknet market, focusing on the impact of item descriptions on sales. In particular, the study examines how text contained in product listings can influence sales and contribute to social order. MethodTo conduct this analysis, we examined 4160 cocaine listings on AlphaBay, which was active from December 2014 to July 2017 and is one of the largest darknet markets in history. Using generalised additive models (GAMs), we assessed the impact of various listing description features, including content and semantic structure, on cocaine sales. ResultsThe results showed that sales increased by 61.6 % when listings included delivery information in their description, compared to offers that did not. In addition, the standardised sentiment score (ranging 0,1) of the product description increased positively, and estimated sales increased by 260.5 %. We also found that international shipping reduced sales by 28.3 %. Finally, we found that listings stating the product origin increased sales for all continents except Asia. ConclusionThe study sheds light on the characteristics of product advertising that facilitate social order within a darknet market. Listings that include delivery details in the description reduce uncertainty about a critical stage of the transaction process while using positive language increases trust. This study makes both an empirical and a theoretical contribution by demonstrating the influence of ad descriptions on sales and the intricate role of social influences in shaping market order.

The next phase of identifying illicit activity in Bitcoin

AbstractIdentifying illicit behavior in the Bitcoin network is a well‐explored topic. The methods proposed over time have generated great insights into the deanonymization of the Bitcoin user base through the clustering of inputs and outputs. With advanced techniques being deployed by Bitcoin users, these heuristics are now being challenged in their ability to aid in the detection of illicit activity. In this paper, we provide a comprehensive list of methods deployed by malicious actors on the network and illicit transaction mining methods. We detail the evolution of the heuristics that are used to deanonymize Bitcoin transactions. We highlight the issues associated with conducting law enforcement investigations and propose recommendations for the research community to address these issues. Our recommendations include the release of public data by exchanges to allow researchers and law enforcement to further protect the network from malicious users. We recommend the enhancement of current heuristics through machine learning methods and discuss how researchers can take the fight head‐on against expert cybercriminals.

GREY FLOWLESS, OR PREDICTING CAPITAL OUTFLOWS IN CRYPTOCURRENCY

Globalization and irrational capital distribution have fuelled global financial crises. Illicit transactions on a global scale worsen financial challenges, limiting government spending on public services. Amid these challenges, blockchain technology and cryptocurrencies have emerged as potential solutions. Due to anonymity public identification through “keys”, some scholars argue that cryptocurrencies create an opportunity for misuse as an easy tool for money laundering, tax evasion and illegal activities. This study investigates the relationship between illicit finance flows and cryptocurrency markets, utilizing grey systems theory and grey relational analysis. Drawing samples from 41 states with the highest cryptocurrency trade volumes, the research reveals nuanced dynamics within the cryptocurrency market, shedding light on the connections between cryptocurrencies, shadow activities, and capital outflows. The findings contribute valuable insights to the ongoing discourse on the impact of cryptocurrencies on global financial stability. The intricate exploration of these interconnections underscores the need for a comprehensive understanding of the role cryptocurrencies play in shaping the contemporary financial landscape.

Robust recurrent graph convolutional network approach based sequential prediction of illicit transactions in cryptocurrencies

Money laundering has urged the need for machine learning algorithms for combating illicit services in the blockchain of cryptocurrencies due to its increasing complexity. Recent studies have revealed promising results using supervised learning methods in classifying illicit Bitcoin transactions of Elliptic data, one of the largest labelled data of Bitcoin transaction graphs. Nonetheless, all learning algorithms have failed to capture the dark market shutdown event that occurred in this data using its original features. This paper proposes a novel method named recurrent graph neural network model that extracts the temporal and graph topology of Bitcoin data to perform node classification as licit/illicit transactions. The proposed model performs sequential predictions that rely on recent labelled transactions designated by antecedent neighbouring features. Our main finding is that the proposed model against various models on Elliptic data has achieved state-of-the-art with accuracy and f1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f_1$$\\end{document}-score of 98.99% and 91.75%, respectively. Moreover, we visualise a snapshot of a Bitcoin transaction graph of Elliptic data to perform a case study using a backward reasoning process. The latter highlights the effectiveness of the proposed model from the explainability perspective. Sequential prediction leverages the dynamicity of the graph network in Elliptic data.

Inappropriate Behavior in Buying and Selling Usernames on Twitter: An Islamic Law Perspective

Transformations in the digital world allow illicit transactions such as buying and selling usernames, which are currently popular among social media users, especially on Twitter. The more people who use Twitter, the more usernames are used, and the system will automatically reject new user requests with usernames that have already been used. This research examined username buying and selling transactions and inappropriate behavior by users on Twitter. This research used qualitative methods with an approach to Islamic legal norms. Data were obtained from document studies and interviews with sellers and buyers of usernames via Twitter direct messages. The results of the research showed that buying and selling usernames are an illegal and prohibited sale because many inappropriate behaviors, such as hacking, are found, resulting in nonfulfillment of the conditions for the objects bought and sold. Islamic law stipulates that traded objects must meet requirements such as perfect ownership, clear, useful, and halal. This means that there is no element of gharar in every buying and selling transaction because it can cause disputes between the seller and the buyer.

حظر استغلال التعامل بالمعلومات الداخلية في قطر: دراسة نقدية مقارنة مع القانون الأمريكي

The purpose of this research is to examine existing loopholes in Qatar’s insider trading laws as impediments to a functional stock market. Weak insider trading rules diminish investor trust in local markets, which may encourage investors on the Qatar Stock Exchange to seek other markets, inhibiting Qatar’s intent to serve as a regional financial hub. This research revolves around proposing new rules to the current insider trading regulations. This critical analytical study gives a breakdown of the Qatari law on insider trading, studying its deterrence, investigation, and punitive components. Comparing these components with US regulations on insider trading, which are globally recognized as effective, this work identifies a key issue in each component of the Qatari law. Based on the US model, this study found that Qatari regulations need: a disclosure system to prevent immediate trading after information publication, a corporate whistleblower system to curb illicit transactions, and a treble damages provision to prevent violators from profiting or avoiding losses. This research offers substantial value to academics, corporations, governments, and investors looking to strengthen corporations and market development in Qatar or similar economies, emphasizing the importance of effective laws on disclosure, whistleblowers, and damages.

Improving Literacy of Forbidden Selling and Buying in Contemporary Transactions Through Service-Learning Method

Sharia economic literacy has not touched much on the practice of illicit buying and selling in contemporary financial transactions. Based on a preliminary survey as well as the request of community leaders, a community service activity that carries out literacy on the said subject is needed to be conducted. The method used is Service-Learning which provides an active learning model, the method of which is not only face-to-face lectures but also provides a sense of responsibility of presentations as part of society. This activity involved 71 participants, of which 51 out of 71 were willing to fill out the questionaire. The result shows that this community service activity has helped participants in understanding prohibited buying and selling in contemporary transactions. The presentation on Sharia provisions in buying and selling improved the awareness and knowledge of illicit transactions. Referring to the activity evaluation, this community service is beneficial, being reflected by the improvement of participants' comprehension and the increased level of literacy before and after the program.

Inadequate Cryptocurrency and Money Laundering Regulations in Indonesia (Comparative Law of US and Germany)

<em>Cryptocurrency as a virtual currency managed by a decentralized system makes it immune to government interference and allows it to transact under pseudonyms. This has the potential for cybercrime and illicit transactions, especially money laundering. This study aims to compare legal instruments in Indonesia, the US, and Germany regarding the use of cryptocurrency as a money laundering tool and to analyze the readiness of Indonesia to respond to this crime. This study is normative legal research conducted using a comparative and statutory approach. These findings show that the US and Germany have extensively regulated crypto. In the US, Crypto transactions are considered MSB, subject to BSA compliance. Each transaction must comply with AML, KYC, and CIP requirements. In Germany, Cryptocurrency is considered a personal asset. The crypto trading must meet the KYC and AML requirements. Indonesia needs advanced regulations because crypto is only considered an investment asset. The investigation is difficult because cryptocurrency is transacted pseudonyms, so connecting pseudonyms with real people is challenging</em>

Fraud Detection of Credit Card Using Machine Learning

Abstract: This paper is focused on credit card fraud detection in real world scenarios. Nowadays credit card frauds are increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed paper we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions.

Keeping Pace With the Evolution of Illicit Darknet Fentanyl Markets: Using a Mixed Methods Approach to Identify Trust Signals and Develop a Vendor Trustworthiness Index

Illicit darknet markets (DNMs) are highly uncertain and in a perpetual state of flux. These markets thrive in a zero-trust, high-risk environment. However, the trustworthiness of vendors plays a critical role in illicit transactions and the sustainability of the illegal trade of goods and services on DNMs. Focusing on the illicit fentanyl trade and applying signaling theory and embedded mixed methods design, we examined different ways that trustworthiness is signaled by vendors on darknet sites. Fentanyl, a synthetic opioid, in recent years, has been declared a public health emergency in the United States due to its high potency and unprecedented number of deaths associated with its use; however, the topic remains understudied and requires urgent attention. There are few studies that have focused on fentanyl trafficking on DNMs and no mixed method studies that have focused specifically on trust signals in DNM fentanyl networks. In our research, first, we conducted a focus group and in-depth interviews with criminal justice professionals to understand the inner workings of darknet sites, fentanyl networks, and how trust is assessed. Second, we scraped select darknet sites to collect and curate scraped data for later examination of vendor trustworthiness on DNMs. Third, using signaling theory to understand how vendors signal trustworthiness on select darknet sites selling drugs, including fentanyl, we applied both qualitative and quantitative content analysis of DNM features, and language used in vendor profiles, listings, and product/vendor reviews, to inform the development of a trustworthiness index. In this research, we used software, such as Atlas.ti and Python, to analyze our data. The main purpose of this article is to provide an in-depth description of the mixed methods approach we used to inform the development of a vendor trustworthiness index, which we used to examine trust between illicit fentanyl vendors and buyers. Our research can serve as a guide for the development of DNM vendor trustworthiness index for future research on other illegal markets.

An Assessment of Cryptomixing Services in Online Illicit Markets

The internet has become a popular marketplace for the sale of illicit products, including stolen personal information, drugs, and firearms. Many of these products are acquired using cryptocurrencies, which are generally defined as forms of digital currency that is traceable through blockchain ledger technology. These currencies are thought to be more secure than other forms of digital payment, though law enforcement and financial service providers have found ways to investigate account holders and their transactions. Consequently, several service providers have begun to offer cryptomixing services, which effectively launders payments to circumvent detection and investigation tools. Few have explored the practices of cryptomixing services, or the ways in which they are marketed on the Open and Dark Web. This inductive qualitative analysis will examine a sample of 18 cryptomixing services advertised on both the Open and Dark Web to better understand cryptomixing and its role in facilitating illicit transactions across the internet.

Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin

Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin, for money laundering. The use of cryptocurrencies can hide criminal identities and transfer hundreds of millions of dollars of dirty funds through their criminal digital wallets. However, this is considered a paradox because cryptocurrencies are goldmines for open-source intelligence, giving law enforcement agencies more power when conducting forensic analyses. This paper proposes Inspection-L, a graph neural network (GNN) framework based on a self-supervised Deep Graph Infomax (DGI) and Graph Isomorphism Network (GIN), with supervised learning algorithms, namely Random Forest (RF), to detect illicit transactions for anti-money laundering (AML). To the best of our knowledge, our proposal is the first to apply self-supervised GNNs to the problem of AML in Bitcoin. The proposed method was evaluated on the Elliptic dataset and shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of self-supervised GNN in the detection of illicit cryptocurrency transactions.

Improving anti-money laundering in bitcoin using evolving graph convolutions and deep neural decision forest

PurposeMoney laundering is the process of concealing unlawfully obtained funds by presenting them as coming from a legitimate source. Criminals use crypto money laundering to hide the illicit origin of funds using a variety of methods. The most simplified form of bitcoin money laundering leans hard on the fact that transactions made in cryptocurrencies are pseudonymous, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. With the motive to curb these illegal activities, there exist various rules, policies and technologies collectively known as anti-money laundering (AML) tools. When properly implemented, AML restrictions reduce the negative effects of illegal economic activity while also promoting financial market integrity and stability, but these bear high costs for institutions. The purpose of this work is to motivate the opportunity to reconcile the cause of safety with that of financial inclusion, bearing in mind the limitations of the available data. The authors use the Elliptic dataset; to the best of the authors' knowledge, this is the largest labelled transaction dataset publicly available in any cryptocurrency.Design/methodology/approachAML in bitcoin can be modelled as a node classification task in dynamic networks. In this work, graph convolutional decision forest will be introduced, which combines the potentialities of evolving graph convolutional network and deep neural decision forest (DNDF). This model will be used to classify the unknown transactions in the Elliptic dataset. Additionally, the application of knowledge distillation (KD) over the proposed approach gives finest results compared to all the other experimented techniques.FindingsThe importance of utilising a concatenation between dynamic graph learning and ensemble feature learning is demonstrated in this work. The results show the superiority of the proposed model to classify the illicit transactions in the Elliptic dataset. Experiments also show that the results can be further improved when the system is fine-tuned using a KD framework.Originality/valueExisting works used either ensemble learning or dynamic graph learning to tackle the problem of AML in bitcoin. The proposed model provides a novel view to combine the power of random forest with dynamic graph learning methods. Furthermore, the work also demonstrates the advantage of KD in improving the performance of the whole system.

From flows towards updates: Security regimes and changing technologies for financial surveillance

Abstract‘Follow the money’ is currently the central principle of international financial security, although money itself is probably one of the most unlikely objects to make traceable. Two recent scandals around a security unit and the payment processor Wirecard show how existing systems of financial surveillance that seek to capture ‘flows’ of money for security purposes are either enabled or frustrated. While this current regime of financial surveillance adheres to demanding the free flow of money through financial infrastructures and various actors and intermediaries, new digital currencies build on a set type of ledger(s) in which money is stored as data. Hence, what we understand as money does not ‘flow’, but is rather updated. This change in the underlying infrastructure means that traceability does not need to be enacted; it is an intrinsic feature of digital currencies. With new central bank digital currencies (CBDC), the regime of financial security thus changes from the monitoring of financial flows and flagging of (potentially) illicit transactions towards the storage of financial data in (de)centralised ledgers. This form of transactional governance is engendered by shifting geopolitical agendas that increasingly rely on fractured instead of globalised financial infrastructures, thus making CBDCs themselves subject to security efforts.