Identifying a Criminal's Network of Trust

Tracing criminal ties and mining evidence from a large network to begin a crime case analysis has been difficult for criminal investigators due to large numbers of nodes and their complex relationships. In this paper, trust networks using blind carbon copy (BCC) emails were formed. We show that our new shortest paths network search algorithm combining shortest paths and network centrality measures can isolate and identify criminals' connections within a trust network. A group of BCC emails out of 1,887,305 Enron email transactions were isolated for this purpose. The algorithm uses two central nodes, most influential and middle man, to extract a shortest paths trust network.Comment: 2014 Tenth International Conference on Signal-Image Technology & Internet-Based Systems (Presented at Third International Workshop on Complex Networks and their Applications,SITIS 2014, Marrakesh, Morocco, 23-27, November 2014

Development of algorithms for searching, analyzing and detecting fraudulent activities in the financial sphere

According to Digital Evolution Index 2017, Russia is included to the category of so-called “Break Out” countries. The major problem to be encountered at transfer to the digital economy is adaptation of new technologies – such as Big Data, Blockchain, Internet of Things, Cryptocurrency, machine learning. No less important field is development of friendly informative environment facilitating international cooperation, cyber safety problems resolving, etc. This example provides the data of the report prototype of a system to detect suspicious transactions. This system shall read and analyze the transaction database and, in accordance with search algorithms, it detects suspicious transactions within the entire data base. The algorithm consists of several stages: development of a graph, selection of suspicious and trusted transactions, calculation of signs and machine learning. The methods of social connections analysis, parallel processing of graphs and mathematical apparatus of neural networks are used as the basis of this research.peer-reviewe

Does Money Laundering on Ethereum Have Traditional Traits?

As the largest blockchain platform that supports smart contracts, Ethereum has developed with an incredible speed. Yet due to the anonymity of blockchain, the popularity of Ethereum has fostered the emergence of various illegal activities and money laundering by converting ill-gotten funds to cash. In the traditional money laundering scenario, researchers have uncovered the prevalent traits of money laundering. However, since money laundering on Ethereum is an emerging means, little is known about money laundering on Ethereum. To fill the gap, in this paper, we conduct an in-depth study on Ethereum money laundering networks through the lens of a representative security event on \textit{Upbit Exchange} to explore whether money laundering on Ethereum has traditional traits. Specifically, we construct a money laundering network on Ethereum by crawling the transaction records of \textit{Upbit Hack}. Then, we present five questions based on the traditional traits of money laundering networks. By leveraging network analysis, we characterize the money laundering network on Ethereum and answer these questions. In the end, we summarize the findings of money laundering networks on Ethereum, which lay the groundwork for money laundering detection on Ethereum

AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.Comment: 38 pages, update reference

Cryptocurrencies as a subject of financial fraud

CEL: Celem głównym niniejszego opracowania jest identyfikacja aktualnego zakresu badań dotyczących kryptowalut jako przedmiotu nadużyć finansowych. Szczegółowe pytania badawcze odnosiły się do prezentacji najważniejszych kierunków tematycznych prowadzonych badań oraz zdefiniowania potencjalnych możliwości dalszej analizy tego tematu. Jedno z pytań wiązało się również z identyfikacją najbardziej popularnych oszustw przeprowadzanych z użyciem kryptowalut. METODYKA: Artykuł opiera się na systematycznym przeglądzie literatury (SLR) przeprowadzonym dla 57 publikacji dostępnych w bazie Scopus. Dokonano bibliometrycznej oraz opisowej analizy wybranych pozycji literatury przedmiotu. Następnie wydzielono główne klastry tematyczne i dokonano pogłębionej analizy ich treści. WYNIKI: Szczegółowa analiza bibliometryczna i opisowa pokazała, że tematyka kryptowalut jako przedmiotu nadużyć finansowych jest generalnie nowym obszarem badań naukowych, choć rozwija się dość intensywnie. Relatywnie mała liczba publikacji w porównaniu z innymi podobnymi obszarami pokazuje również, że ten temat nie jest jeszcze tak mocno eksplorowany przez naukowców i można w nim rozwijać wiele różnych trendów badawczych. Ostatecznie zidentyfikowano następujące kluczowe obszary badawcze: rodzaje oszustw kryptowalutowych, metody wykrywania nadużyć, ryzyka związane z technologią blockchain, pranie brudnych pieniędzy oraz regulacje prawne dotyczące kryptowalut. Udało się również ustalić, że obecnie najczęściej występującym przestępstwem jest pranie pieniędzy. Zwrócono jednak uwagę, że drugim dość częstym oszustwem są piramidy finansowe oparte na schemacie Ponziego. IMPLIKACJE: W artykule wyraźnie przedstawiono główne trendy badawcze dotyczące wykorzystania kryptowalut w działalności przestępczej. Jednocześnie podkreślono, że w porównaniu do innych obszarów badawczych niniejsza tematyka jest stosunkowo nowa. Powstaje zatem szeroka możliwość eksploracji nie tylko istniejących, ale również nie odkrytych do tej pory nurtów badawczych. Ponadto zidentyfikowano kluczowe rodzaje oszustw w praktyce gospodarczej, co jest szczególnie istotne dla uczestników rynków finansowych. Wyraźnie wskazano bowiem, które transakcje są obarczone największym ryzykiem. Warto również zwrócić uwagę na istotną aktualność tematu, gdyż skala przestępczości z udziałem kryptowalut ostatnio gwałtownie rośnie. Opracowanie potwierdza niedostateczny zakres regulacji prawnych, które nie są w stanie odpowiednio wzmocnić bezpieczeństwa obrotu gospodarczego. Może być zatem jasnym wskazaniem dla rządów poszczególnych państw, czy też instytucji międzynarodowych do dalszych sprawnych zmian przepisów prawa. ORYGINALNOŚĆ I WARTOŚĆ: Naukowy wkład niniejszego opracowania jest potrójny. Po pierwsze, jest to jeden z pierwszych artykułów badawczych prezentujący wyniki systematycznego przeglądu literatury (SLR) połączonego z analizą bibliograficzną oraz pogłębioną analizą treści publikacji. Podczas pracy zastosowano również oprogramowanie VOSviewer, które umożliwiło obiektywną identyfikację głównych klastrów tematycznych opartą na occurrences and link strength of keywords ujętych w publikacjach. Po drugie, zidentyfikowano kluczowe rodzaje oszustw, które jednocześnie powodują największe straty finansowe. Wyznaczono również kierunki dalszych badań, które mają głębokie praktyczne implikacje dla uczestników rynku. Niektóre z nich dotyczą bowiem konieczności opracowywania i wdrażania nowoczesnych aplikacji komputerowych, pozwalających na wykrywanie szerszego zakresu pojawiających się nadużyć.PURPOSE: The main purpose of this paper was to identify the current scope of research on cryptocurrencies as a subject of fraud. Detailed research questions related to the determination of contemporary trends of the conducted research and the definition of potential opportunities for further investigation of this topic. One of the questions also concerned identifying the most common crimes committed using cryptocurrencies. METHODOLOGY: The study is based on a systematic literature review (SLR) of 57 publications available on the Scopus database. A bibliometric and descriptive analysis of selected literature items was carried out. Then, vital thematic clusters were separated, and an in-depth content analysis was performed. FINDINGS: The detailed bibliometric and descriptive analysis showed that cryptocurrencies as a subject of financial fraud are generally a new area of scientific research, although it is developing quite intensively. The relatively small number of publications, compared to other similar areas, also indicates that this topic has not yet been explored widely by scientists, and many different research trends can be created in it. Ultimately, the following key research areas were identified: types of cryptocurrency fraud, crime detection methods, risks related to blockchain technology, money laundering, and legal regulations regarding cryptocurrencies. It was also possible to identify that money laundering is currently the most common fraud. However, it has been pointed out that the second most frequent fraud is financial pyramids based on the Ponzi scheme. IMPLICATIONS: The paper clearly presents the main research trends on using cryptocurrencies in criminal activities. At the same time, it was emphasized that, compared to other research areas, this topic is relatively new. Therefore, there is a wide possibility of exploring not only existing but also undiscovered research trends. In addition, key types of fraud in economic practice have been identified, which is particularly important for financial market participants. It was clearly indicated which transactions bear the highest risk. It is also worth paying attention to the critical timeliness of the topic, as the scale of crimes involving cryptocurrencies has recently been growing rapidly. The study confirms the insufficient scope of legal regulations, which are not able to strengthen the security of economic transactions adequately. Therefore, it can be a clear indication for the governments of individual countries or international institutions for further efficient changes to the law. ORIGINALITY AND VALUE: The contribution of this study is threefold. It is one of the first research papers showing the results of a systematic literature review (SLR) combined with a bibliographic and in-depth analysis of the content of publications in this field. During the work, the VOSviewer software was also used, which enabled objective identification of the main thematic clusters based on the occurrences and link strength of keywords included in the publications. Secondly, the key types of fraud have been identified that, at the same time, cause the most significant financial loss. This allowed for the establishing of directions for further research, which have profound practical implications for market participants. Some of them relate to the need to develop and implement modern computer applications, allowing for the detection of a wider range of emerging abuses

Graph Mining for Cybersecurity: A Survey

The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance. It is imperative to summarize existing graph-based cybersecurity solutions to provide a guide for future studies. Therefore, as a key contribution of this paper, we provide a comprehensive review of graph mining for cybersecurity, including an overview of cybersecurity tasks, the typical graph mining techniques, and the general process of applying them to cybersecurity, as well as various solutions for different cybersecurity tasks. For each task, we probe into relevant methods and highlight the graph types, graph approaches, and task levels in their modeling. Furthermore, we collect open datasets and toolkits for graph-based cybersecurity. Finally, we outlook the potential directions of this field for future research

MODEL FOR MONEY MULE RECRUITMENT IN MALAYSIA: AWARENESS PERSPECTIVE

Technology advancement has taken a new shape in leading the world into digital civilization, remarkably in e-commerce, communication, and financial sectors.  Taking advantage of the technology, criminals have also digitalized their modus operandi targeting the digital society with fraud and cybercrimes, hence contributing illicit funds. To disguise the money trail to the illegal activities, illicit funds sourced from unlawful and fraudulent activities transformed into legal funds via a money-laundering scheme. Money laundering is perceived as a global threat where funds reverted to the criminal and enter a legitimate economy. To enable the criminals, maintain anonymity, and non-visible to the detection of law enforcement, money mules are positioned in the money laundering chain between actual criminal and illicit funds. Money mules are characters recruited by criminal networks to perform fund transfers by utilizing their accounts. Recruitment is done by offering a job with simple recruitment criteria and attractive income and rewards. This study will examine money mule recruitment and development of a model with related variables to establish the relationship between the aspect of job criteria awareness and the ability of the victims to detect the hidden criminal elements and exposure to law enforcement and derive into the decision to accept the job offer. In this research, both quantitative and qualitative approaches will be employed with surveys and interviews

Designing a relational model to identify relationships between suspicious customers in anti-money laundering (AML) using social network analysis (SNA)

The stability of the economy and political system of any country highly depends on the policy of anti-money laundering (AML). If government policies are incapable of handling money laundering activities in an appropriate way, the control of the economy can be transferred to criminals. The current literature provides various technical solutions, such as clustering-based anomaly detection techniques, rule-based systems, and a decision tree algorithm, to control such activities that can aid in identifying suspicious customers or transactions. However, the literature provides no effective and appropriate solutions that could aid in identifying relationships between suspicious customers or transactions. The current challenge in the field is to identify associated links between suspicious customers who are involved in money laundering. To consider this challenge, this paper discusses the challenges associated with identifying relationships such as business and family relationships and proposes a model to identify links between suspicious customers using social network analysis (SNA). The proposed model aims to identify various mafias and groups involved in money laundering activities, thereby aiding in preventing money laundering activities and potential terrorist financing. The proposed model is based on relational data of customer profiles and social networking functions metrics to identify suspicious customers and transactions. A series of experiments are conducted with financial data, and the results of these experiments show promising results for financial institutions who can gain real benefits from the proposed model

Financial Crimes in Web3-empowered Metaverse: Taxonomy, Countermeasures, and Opportunities

At present, the concept of metaverse has sparked widespread attention from the public to major industries. With the rapid development of blockchain and Web3 technologies, the decentralized metaverse ecology has attracted a large influx of users and capital. Due to the lack of industry standards and regulatory rules, the Web3-empowered metaverse ecosystem has witnessed a variety of financial crimes, such as scams, code exploit, wash trading, money laundering, and illegal services and shops. To this end, it is especially urgent and critical to summarize and classify the financial security threats on the Web3-empowered metaverse in order to maintain the long-term healthy development of its ecology. In this paper, we first outline the background, foundation, and applications of the Web3 metaverse. Then, we provide a comprehensive overview and taxonomy of the security risks and financial crimes that have emerged since the development of the decentralized metaverse. For each financial crime, we focus on three issues: a) existing definitions, b) relevant cases and analysis, and c) existing academic research on this type of crime. Next, from the perspective of academic research and government policy, we summarize the current anti-crime measurements and technologies in the metaverse. Finally, we discuss the opportunities and challenges in behavioral mining and the potential regulation of financial activities in the metaverse. The overview of this paper is expected to help readers better understand the potential security threats in this emerging ecology, and to provide insights and references for financial crime fighting.Comment: 24pages, 6 figures, 140 references, submitted to the Open Journal of the Computer Societ