Purpose The purpose of this paper is to examine the currently known techniques to tackle money laundering in Bitcoin mixers, and examine what gaps exist that would allow a criminal to get away with laundering Bitcoin obtained through illicit activities. Design/methodology/approach This paper first establishes the relevant properties of Bitcoin, how transactions occur over the Bitcoin network and then introduces the Bitcoin transaction graph as an important data structure for any analysis of Bitcoin transactions. Next, the paper outlines how Bitcoin mixing works, along with the relevant properties of mixers that would be relevant for money laundering. The paper then assesses the known methods for identifying mixed transactions within the Bitcoin network, followed by an assessment on identifying money laundering activities on known mixed transactions. Findings This paper argues that there remains a gap for criminals to launder money through Bitcoin mixing services as known methods would unlikely be able to trace a tainted transaction that goes through a decentralized mixer that uses off-chain communication techniques to coordinate the mixing and charges randomized mixing fees. Research limitations/implications The study of known methods is restricted to literature published in the public domain. There are private organizations that are tackling similar problems, but their methods are not published and therefore cannot be included in this paper. Originality/value To best of the author’s knowledge, this is the first paper that performs a contemporaneous review on anti-money laundering in the context of Bitcoin mixing. This paper could assist regulators and policymakers in their understanding of Bitcoin mixers and provide guidance on where they should focus their resources to address the money laundering problem of Bitcoin mixing.
Oumaima Fadi, Karim Zkik, El Ghazi Abdellatif, Mohammed Boulmalf
Smart environments consist of a collection of sensors, actuators, and numerous computing units that improve human life. With the booming of smart environments, data generation has been notably increasing in recent years, which must be managed in a smart and optimal manner. The components (i.e., workstations and cloud) used for data processing are not the best to recommend since it is risky and resource costing. For that matter, enterprises, firms and companies are deploying blockchain technologies (BT) as a more suitable alternative. In fact, blockchain is a distributed transaction ledger ensuring the reliability and transparency of data. However, BT faces some inherent security challenges such as DoS, eclipse and double spending attacks as well as Advanced Persistent Threat (APT) and malware. Thus, advanced anomaly detection and mitigation approaches, especially the ones using artificial intelligence (AI) techniques (e. g. Machine Learning, Deep Learning, Federated Learning) are required to address the aforementioned issues. In combination, AI and BT are capable of detecting anomalies within blockchain networks with high accuracy. In this paper, with a focus on cyber security issues, we explore the challenges of blockchain deployment in smart environments. Additionally, we explore the use of anomaly detection AI-based techniques as a ledger of blockchain technologies to address the security issues in smart environments. Thus, we propose a framework that emphasizes the challenges of BT, values and capabilities of BT-AI integration. We also present research trends to highlight potential research paths for improving the security of blockchain networks using artificial intelligence.
The interest in cryptocurrency investing is constantly growing. Cryptocurrency may be the currency of the future, but it is also the heaven for con artists to scam investors from their money. Crypto transactions are irreversible. If the underlying blockchain technology has privacy or mixer capabilities it can be virtually untraceable, which creates a new avenue for criminals to scam victims with ease. Social media impersonation is one of the top scams currently performed by criminals. This study presents an example of a social media impersonation scam and the characteristics of the scam. The qualitative data is gathered from communication between the scammer and the potential victim. This study also indicates that cryptocurrency awareness should be included in cyber security training curriculums.
Bitcoin is the most widely used cryptocurrency for illegal trade in current darknet markets. Owing to the anonymity of its addresses, even though transaction flows are globally visible, Bitcoin clustering remains one of the most challenging and open problems in illegal Bitcoin transaction analysis. In this article, to resolve this problem, we propose a novelmulti-layer heuristicalgorithm for Bitcoin clustering, which leverages on-chain transactions as well as off-chain application data in the real world. For this purpose, we first explored the unique characteristics of darknet market ecosystems including their trading systems. By conducting an in-depth analysis of the data manually collected for 11 months, we found that some darknet market review data disclosed transactions containing Bitcoin value and item delivery information. We then identified unique Bitcoin addresses associated with the disclosed information, owned by the same darknet providers. Based on address ownership, more accurate market clusters could be created, which have not previously been identified by other clustering algorithms. According to our experimental results, approximately 31.68% of the darknet market review data matched real Bitcoin transactions, and 122 hidden clusters associated with Silk Road 4 were found. This indicates that the proposed algorithm can complement existing clustering methods and significantly reduce the false negative rate by up to 91.7%.
With the proliferation of the blockchain technology ecosystems such as mining pools, crypto exchanges, full Bitcoin nodes, wallets, and pool protocol servers in recent years, the denial of service (DoS) attack vector has become more prevalent, and the attacks are targeted to the peer-to-peer networks and blockchain users. Despite blockchain enhancing security with decentralized design, secured distributed storage, and privacy, it is still vulnerable to new attack threats. If an attempted DoS is successful on blockchain, the impact is most likely massive given the fact that it is predominantly used for finance applications. An extensive account of the current state-of-the-art for possible DoS and corresponding mitigation techniques is not discussed in the existing research. This paper analyzes and categorizes the existing state-of-the-art DoS attack methods, detection techniques, and mitigation solutions targeting blockchain peer-to-peer networks as well as conventional network crypto exchanges. The review of the prior research shows that the blockchain ecosystem can be a target to successfully perform DoS attacks in the future, and technological advancements in blockchain are needed to mitigate potential attacks.
According to previous research, cryptocurrency is a driver of money laundering and is associated with several risks (Fletcher, Larkin, & Corbet, 2021; Teichmann & Falker, 2020; Tsuchiya & Hiramoto, 2021). As a result, the purpose of this paper is to concentrate on empirical research in the accounting and finance fields that deal with the impact of cryptocurrencies on the phenomenon of money laundering. To identify relevant literature, we use the following keywords including “cryptocurrency or digital money” and “bitcoin and money laundering”. We identify 28 research papers published between 2011 and 2021. The findings of the studies that were reviewed emphasized the importance of developing a legal framework for digital currencies. Furthermore, it was revealed that all stakeholders play an important role in lowering the risk of money laundering and illicit activities. The findings highlight the critical role that banks, regulators, and all stakeholders play in reducing money laundering risks. These findings may have policy implications for governments aiming to improve cryptocurrency laws and regulations by enforcing financial security standards and laws and monitoring individuals’ and firms’ compliance with them. The review identifies some of the literature’s limitations and suggests future research directions
Nowadays, health insurance has become an essential part of people’s lives as the number of health issues increases. Healthcare emergencies can be troublesome for people who can’t afford huge expenses. Health insurance helps people cover healthcare services expenses in case of a medical emergency and provides financial backup against indebtedness risk. Health insurance and its several benefits can face many security, privacy, and fraud issues. For the past few years, fraud has been a sensitive issue in the health insurance domain as it incurs high losses for individuals, private firms, and governments. So, it is essential for national authorities and private firms to develop systems to detect fraudulent cases and payments. A high volume of health insurance data in electronic form is generated, which is highly sensitive and attracts malicious users. Motivated by these facts, we present a systematic survey for Artificial Intelligence (AI) and blockchain-enabled secure health insurance fraud detection in this paper. This paper presents a taxonomy of various security issues in health insurance. We proposed a blockchain and AI-based secure and intelligent system to detect health insurance fraud. Then a case study related to health insurance fraud is presented. Finally, the open issues and research challenges in implementing the blockchain and an AI-empowered health insurance fraud detection system is presented.
In recent years, the application of virtual currency has become a part of people's life. The decentralization and anonymity of Bitcoin have made it a favorite tool for many criminals. Therefore, how to trace illegal activities in Bitcoin transactions has become one of the most important research areas. This paper systematically collects 25 research results in this field since 2018, and divides them into three areas, i.e., supervised learning, unsupervised learning, and topological analysis. The supervised learning method based on machine learning is the current mainstream in this research field. However, we believe that the model can achieve more accurate results after combining unsupervised learning and topological analysis features. Moreover, topology analysis can help to observe the entire or specific part of the Bitcoin trading network from a macro perspective so as to discover the hidden illegal activities. In addition, data visualization techniques can provide structural insights to understand the Bitcoin trading network.
In recent years, Ethereum, one of the leading applications to realize the service of blockchain technology, has received a great deal of attention with the usability and functionality to execute smart contracts, arbitrary programmable calculations in addition to cryptocurrency trading. However, misconfigured Ethereum clients with application programming interface (API) enabled, JSON-RPC in particular, are targeted by cyberattacks. In this research, we propose a new framework to detect malicious and suspicious Ethereum accounts using 3 different data sources (honeypot, Internet-wide scanner and blockchain explorer). The honeypot, named Etherpot, utilizes a proxy server placed between a real Ethereum client and the Internet. It modifies responses from the Ethereum client to attract attackers, identifies malicious accounts and analyzes their behaviors. With the Internet-wide scan results from Shodan, we also detect suspicious Ethereum accounts that are registered on multiple nodes. Finally, we utilize Etherscan, a well-known blockchain explorer for Ethereum, to track and analyze the activities related to the detected accounts. Through the observation of 6 weeks, we observed 538 hosts trying to call JSON- RPC of our honeypots with 41 different types of methods, including 2 types of unreported attacks in the wild. We detected 16 malicious accounts from the honeypots and 64 suspicious accounts from Shodan scan results, 5 out of which are overlapped. Finally, from Etherscan, we collected records of activities related to the detected accounts, including transactions of 21.50 ETH and mining of 22.61 ETH (equivalent to 167,560 USS at the rate of 2021/10/14). To an end, we provide a much brighter view of malicious activities on Ethereum.
Blockchain technology supports the generation and record of transactions, and maintains the fairness and openness of the cryptocurrency system. However, many fraudsters utilize smart contracts to create fraudulent Ponzi schemes for profiting on Ethereum, which seriously affects financial security. Most existing Ponzi scheme detection techniques suffer from two major restricted problems: the lack of motivation for temporal early warning and failure to fuse multi-source information finally cause the lagging and unsatisfactory performance of Ethereum Ponzi scheme detection. In this paper, we propose a dual-channel early warning framework for Ethereum Ponzi schemes, named Ponzi-Warning, which performs feature extraction and fusion on both code and transaction levels. Moreover, we represent a temporal evolution augmentation strategy for generating transaction graph sequences, which can effectively increase the data scale and introduce temporal information. Comprehensive experiments on our Ponzi scheme datasets demonstrate the effectiveness and timeliness of our framework for detecting the Ponzi contract accounts.
Da Chen, Lin Feng, Yuqi Fan, Siyuan Shang · 5 authors
It is imperative to assure the security of smart contracts via intelligent vulnerability detection tools before deploying smart contracts on blockchains. The existing deep learning-based approaches fail to effectively capture the rich syntax and semantic information embedded in smart contracts. In this paper, we detect smart contract vulnerabilities at the function level by constructing a novel semantic graph (SG) for each function and learning the SGs using a new graph convolutional network EA-RGCN. Our proposed method consists of three stages. In the first stage, we create the SG which characterizes rich data-data, instruction-instruction and instruction-data relationships in the function code. In the second stage, we propose EA-RGCN which contains three parts: node and edge representation via word2vec, content feature extraction with a residual GCN (RGCN) module, and semantic feature extraction using an edge attention (EA) module. Finally, we concatenate the code content features and the semantic features to obtain the global code feature and use a Multilayer Perceptron (MLP) to identify whether the function is vulnerable. We conduct experiments on the dataset constructed from real-world smart contracts. Experimental results demonstrate that the proposed semantic graph and the EA-RGCN model can achieve superior performance.
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.
Jie Cai, Bin Li, Jiale Zhang, Xiaobing Sun · 5 authors
Smart contract security has drawn extensive attention in recent years because of the enormous economic losses caused by vulnerabilities. Even worse, fixing bugs in a deployed smart contract is difficult, so developers must detect security vulnerabilities in a smart contract before deployment. Existing smart contract vulnerability detection efforts heavily rely on fixed rules defined by experts, which are inefficient and inflexible.To overcome the limitations of existing vulnerability detection approaches, we propose a GNN based approach for smart contract vulnerability detection. First, we construct a graph representation for a smart contract function with syntactic and semantic features by combining abstract syntax tree (AST), control flow graph (CFG), and program dependency graph (PDG). To further strengthen the presentation ability of our approach, we perform program slicing to normalize the graph and eliminate the redundant information unrelated to vulnerabilities. Then, we use a Bidirectional Gated Graph Neural-Network model with hybrid attention pooling to identify potential vulnerabilities in smart contract functions.
Md. Nur Islam, Md. Golam Shakhawat Hossen, Samson P. Baidya, Md. Ahsan Ullah Emon · 5 authors
Bitcoin stores its transaction details in an open distributed public ledger. In cryptocurrencies, the real identity of a user is hidden, and the only information about a user publicly available is his public address, which is not linkable to his real identity, and the transactions are sent to the public address. Among all the cryptocurrencies, bitcoin is renowned for providing the highest anonymity. This feature is exploited by the scammers and illegal users to perform their illegal transactions anonymously. To rein in such illegal activities, it is essential to expose the real identity of bitcoin users. In this paper, we propose a technique to trace the real identity of a bitcoin user. In this technique, the blockchain maintains a public ledger where every transaction of that chain is recorded and anyone within the blockchain can monitor that. The proposed platform will have a tracker for tracking a bitcoin user. A victim submits a complain to the platform giving the pubic address of the scammer. The platform continues to track the scammer. The proposed technique exposes the user identity exploiting the bitcoin and real-world currency conversion scenarios. While the existing transaction tracing techniques require the IP address and/or absence of the mixing services, the proposed technique is free of such kinds of requirements.
Kai Wang, Jun Pang, Ding-Jie Chen, Yu Zhao · 7 authors
Exploiting the anonymous mechanism of Bitcoin, ransomware activities demanding ransom in bitcoins have become rampant in recent years. Several existing studies quantify the impact of ransomware activities, mostly focusing on the amount of ransom. However, victims’ reactions in Bitcoin that can well reflect the impact of ransomware activities are somehow largely neglected. Besides, existing studies track ransom transfers at the Bitcoin address level, making it difficult for them to uncover the patterns of ransom transfers from a macro perspective beyond Bitcoin addresses. In this article, we conduct a large-scale analysis of ransom payments, ransom transfers, and victim migrations in Bitcoin from 2012 to 2021. First, we develop a fine-grained address clustering method to cluster Bitcoin addresses into users, which enables us to identify more addresses controlled by ransomware criminals. Second, motivated by the fact that Bitcoin activities and their participants already formed stable industries, such as Darknet and Miner , we train a multi-label classification model to identify the industry identifiers of users. Third, we identify ransom payment transactions and then quantify the amount of ransom and the number of victims in 63 ransomware activities. Finally, after we analyze the trajectories of ransom transferred across different industries and track victims’ migrations across industries, we find out that to obscure the purposes of their transfer trajectories, most ransomware criminals (e.g., operators of Locky and Wannacry) prefer to spread ransom into multiple industries instead of utilizing the services of Bitcoin mixers. Compared with other industries, Investment is highly resilient to ransomware activities in the sense that the number of users in Investment remains relatively stable. Moreover, we also observe that a few victims become active in the Darknet after paying ransom. Our findings in this work can help authorities deeply understand ransomware activities in Bitcoin. While our study focuses on ransomware, our methods are potentially applicable to other cybercriminal activities that have similarly adopted bitcoins as their payments.
Blockchain technology in forensic science can have meaningful changes and improvements in digital forensics, forensic investigation, evidence management, and chain of evidence. The consolidation of research on the application of blockchain technology on forensic management, basing Scopus database was conducted and the research trends of bibliometric analysis were visualized. The prominent funding agencies, leading research organizations, authors, countries, journals, and keywords were mentioned in this research. The future scope of research on this research domain can be on enhancing the privacy, authenticity, reliability, and evidence handling in forensic science by the implementation of blockchain technology.