Nhung Nguyen, An Tuan Nguyen, Ha Thi Nguyet To, Thanh Ta Hong Le
Purpose This paper aims to explore factors influencing Vietnamese people’s susceptibility to fraud through cryptocurrency Ponzi schemes. Design/methodology/approach This study uses the gullibility theory, the theory of planned behavior theory, the traditional theory of finance and the theory of financial behavior, to develop a questionnaire which is then sent to respondents who are Vietnamese individuals. Subsequently, the partial least squares structural equation modeling approach (PLS-SEM) is used to analyze 370 collected responses. Findings This research shows the important roles that trust, risk appetite and knowledge of Ponzi play in respect of fraud susceptibility, among which trust has the highest positive impact. Moreover, there is no evidence of relationships between Vietnamese people’s susceptibility to fraud via cryptocurrency Ponzi schemes and attitudes toward investment scams, knowledge of investment or knowledge of Ponzi schemes. Research limitations/implications This paper collects only 370 valid responses, which raises some questions regarding the diversity and representativeness of the survey sample. Practical implications This study provides evidence on factors affecting Vietnamese people’s fraud susceptibility to cryptocurrency Ponzi schemes, which helps both authorities and individuals to be vigilant against investment scams. Social implications This research proposes several recommendations to prevent investment scams in cryptocurrency trading, from the perspective of state regulators and individuals. Originality/value This working paper provides a new approach using PLS-SEM to build a theoretical framework for the possibility of becoming victims of investment scams in Vietnam using a combination of different theories from criminology and finance.
Bank record security management is vital to the financial security of every nation. It aims to safeguard residents' as well as government income, determines eligibility for social assistance, and has a substantial influence on a country's foreign relations, economy and reputation. Numerous efforts are now ongoing throughout the globe to increase and improve security in the financial industry, especially in developing nations where demand is highest. It is common knowledge that the qualities of blockchain technology promote efficiency, transparency, and confidence. Concerns over data security, the danger of cybercrime, and the efficacy of bank system security are growing, especially in nations like Zambia that are still developing. Even after years later, it seems the banks and Ministry of Finance are unable to verify and assure the country's money is 100% secure, even inside the banks, according to information acquired and based on several interviews about bank security, the existence of a firewall is where all faith lies. Even after registration, there is no guarantee that client information is entirely secure and inaccessible to unauthorized third parties. This raises concerns and dangers about deceit being possible. In Zambia's bank record security management, there have been instances of illegal data changes and more recently, a cybercrime-related data breach. Throughout the years, government and public monies have been misappropriated without any documents to indicate what transpired or what happened to the revenue, and who knows what else. If the institution's information system is made safer and more dependable, process verification problems may be resolved. This research has been engaged with the aim of developing model design that employs blockchain technology principles to enhance Zambia's Record Security Management in banks. The later section of this research has an inclusion of a summary of the various literature about blockchain technology and Bank Record Security Management that is relevant. It describes the standard registry, storage, and operations, as well as a general description of blockchain, including its characteristics and uses. It demonstrates that using blockchain technology might boost efficiency by decreasing human labour and paper processing, and by giving clear provenance that verifies the origin of products. It may be used in industries such as health, education, agriculture, real estate, Bank record Security Management, etc. It also proves that smart contracts have a vast future potential, and that Ethereum is one of the most suitable platforms for constructing distributed applications using smart contracts. To fulfil the objective of the research, it was important to investigate Zambia's Record Security Management procedures in order to uncover continuing problems related with unauthorized alterations and other reported concerns. The Bank Blockchain Adoption Framework guided the examination of process mapping, stages and data. The results allow us to break Bank RSM into three phases: registration, verification and lastly, storage. Using a flowchart process diagram, network architecture, use cases, and technological development diagrams, a full design solution is shown. This project in design science offers a blockchain-based application architecture that will enhance the existing Bank Record Security Management System. It is proposed that the blockchain network be used to share datasets between banks, other financial organizations, and other informatics under the Ministry of Home Affairs Security. On the other hand, a centralized server will be an operating node in the blockchain network that is where records will be stored digitally. The research built a novel registration method on the Ethereum blockchain on Binance Smart Chain using the React App client, which in this case is the NodeJS server's web interface. The architecture was used to assess a smart contract's verification implementation. The NodeJS Server uses the web3 library plugin to interface with the Ethereum client network. Bank Record Security Management based on blockchain technology may enhance the security and integrity of banks and nations. This study's contribution may be utilized for additional research in each of the aforementioned application areas to uncover potential benefits and drawbacks.
This chapter provides an overview of crimes involving cryptocurrencies. It first introduces the concepts of crypto-enabled and crypto-dependent crimes, which offer a framework for categorizing the types of crime. The chapter also describes the characteristics of cryptocurrencies which may facilitate their use by criminals. It then gives an overview of the various types of crime that involve cryptocurrencies, including types of fraud, cyber-crimes, cryptocurrency mining crimes, money laundering, terrorism financing, sanctions evasion, tax evasion, darknet marketplaces, bribery and corruption, and cryptocurrency-adjacent crimes. Finally, the chapter considers crimes associated with particular areas of the larger cryptocurrency ecosystem, namely, decentralized finance (DeFi) and non-fungible tokens (NFTs), and offer conclusions about cryptocurrencies and criminal activity. Many DeFi projects and protocols are governed in a decentralized manner through decentralized autonomous organizations. NFTs are digital records of ownership and authenticity of some asset (which may be digital or physical) created by smart contracts and stored on a blockchain.
Pablo Santamaría, Llanos Tobarra, Rafael Vargas, Antonio Robles-Gómez
The digital revolution is renewing many aspects of our lives, which is also a challenge in judicial processes, such as the Chain-of-Custody (CoC) process of any electronic evidence. A CoC management system must be designed to guarantee them to maintain its integrity in court. This issue is essential for digital evidence’s admissibility and probative value. This work has built and validated a real prototype to manage the CoC process of any digital evidence. Our technological solution follows a process model that separates the evidence registry and any evidence itself for scalability purposes. It includes the development of an open-source smart contract under Quorum, a version of Ethereum oriented to private business environments. The significant findings of our analysis have been: (1) Blockchain networks can become a solution, where integrity, privacy and traceability must be guaranteed between untrustworthy parties; and (2) the necessity of promoting the standardization of CoC smart contracts with a secure, simple process logic. Consequently, these contracts should be deployed in consortium environments, where reliable, independent third parties validate the transactions without having to know their content.
Tema završnog rada je utjecaj kriptovaluta na međunarodno poslovanje. Nalazimo se u digitalnom dobu, svjedoci smo velikih promjena, pa tako i onih ekonomskih. Tržište kriptovaluta cvjeta te nastavlja rasti enormnom brzinom. Kriptovalute su svakako dio naše budućnosti, iako nisu savršene i imaju svojim mana, zasigurno primjećujemo njihove prednosti pogotovo u međunarodnom poslovanju, olakšavaju prekograničnu trgovinu i smanjuju transakcijske troškove. Cilj rada je objasniti pojam i nastanak kriptovaluta te njihov utjecaj na međunarodno poslovanje. U radu su dani primjeri korištenja kriptovaluta, opisane su prednosti i nedostaci njihova korištenja u odnosu na tradicionalne financijske sustave.
Wash trading is considered a highly inopportune and illegal behavior in regulated markets. Instead, it is practiced in unregulated markets, such as cryptocurrency or NFT (Non-Fungible Tokens) markets. Regarding the latter, in the past many researchers have been interested in this phenomenon from an “ex-ante” perspective, aiming to identify and classify wash trading activities before or at the exact time they happen. In this paper, we want to investigate the phenomenon of wash trading in the NFT market from a completely different perspective, namely “ex-post”. Our ultimate goal is to analyze wash trading activities in the past to understand whether the game is worth the candle, i.e., whether these illicit activities actually lead to a significant profit for their perpetrators. To the best of our knowledge, this is the first paper in the literature that attempts to answer this question in a “structured” way. The efforts to answer this question have enabled us to make some additional contributions to the literature in this research area. They are: (i) a framework to support future “ex-post” analyses of the NFT wash trading phenomenon; (ii) a new dataset on wash trading transactions involving NFTs that can support further future investigations of this phenomenon; (iii) a set of insights of the NFT wash trading phenomenon extracted at the end of an experimental campaign.
The banking sector is identified as the main means for laundering illicit money, these banks generally have access to both banking mechanism and legal authority to make decisions. Money launderers and those financing terrorism are conveniently accessing financial institutions and its mechanism. These institutions provide all financial funds transfers both domestic and international range. Anti-money laundering (AML) laws and other data protection laws that keep getting stricter have forced many financial institutions to put in place long, expensive processes to stay in compliance. To bridge the gap, emerging technology can help in mitigating money laundering and other financial crimes. Blockchain is considered one of the world's best-known examples of Distributed Ledger Technology. In the financial sector, this type of technology has been hailed as the key to future success. Emerging technology can be used in many ways in Financial Services. It can change many processes, payments between peer-to-peer, trade agreements and tracking of supply chains. Emerging technology can be used in many ways in financial services and can change many processes, peer-to-peer payments, trade agreements, and the tracking of supply chains. These use cases depend on the participants or users being identified and verified. "Know Your Customer" is the term for this (KYC). Before making a transaction, one of the most basic ways to build trust between the people involved is to check out the user. This current paper focuses on issues of compliance and Anti Money Laundering policies in the banking sector by using new emerging technologies such as blockchain; further, the paper focuses keenly on issues relating to the manipulation of KYC and the financial burden on banks while also addressing AML policies, Finally, the paper provides ideas and suggestions regarding the rise of emerging technologies such as blockchain while addressing the problems of ML. This also includes the capability of blockchain technology to bring banking systems with recalibrated mode of compliances polices.
Gaurav Dhiman, Marcello Carvalho dos Reis, Paulo C. S. Barbosa, Victor Hugo C. de Albuquerque · 5 authors
Abstract Retraction: [Gaurav Dhiman, Marcello Carvalho dos Reis, Paulo C. S. Barbosa, Victor Hugo C. de Albuquerque, Sandeep Kautish, Blockchain‐based covert software information transmission for bitcoin, IET Software 2023 ( https://doi.org/10.1049/sfw2.12120 )]. The above article from IET Software , published online on 8 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
At present, blockchain is in a period of rapid development. However, there are many hidden dangers in its development process. This paper completed the analysis and verification after studying various indicators of the Bitcoin network and putting forward hypotheses based on experience. It is found that the correlation between bitcoin network trading volume and the amount of account funds remains stable. Most of the early saving accounts are actually still transaction participants in the current ecosystem. At present, most bitcoin transaction network entity recognition and classification methods are still based on network graph structure features. This paper proposed an entity recognition and classification method based on the relevance features by starting from the characteristics of bitcoin’s own multi-transaction association. Some data are obtained from public websites, and it shows that the method in experiments could improve the performance of entity recognition effectively. In addition, this paper also analyzes the importance of features and the relevance of classification. In order to deal with the above transaction network feature analysis and entity recognition of the actual demand, this paper also designed a set of highly automated, timely interruption and recovery, and abnormal alarm function of the system. It can be migrated to other Bitcoin networks, and it is very convenient and efficient. This paper can improve the performance of entity recognition and make our understanding of Bitcoin deeper. This system has great theoretical value and application value for the research of bitcoin de-anonymization.
Financial enterprises generate profits based on economic development. More importantly, a healthy market is difficult to achieve due to their susceptibility to the parasitic credit card fraud transactions that accompany economic growth, unless an effective anti-counterfeiting technology is developed to alleviate the issue. To solve the problem, we propose a gradient-boosting decision tree based anti-fraud protection with blockchain Technology, referred to as GBDT-APBT, which treats anti-fraud transaction model as the accumulation of the classfiers' weakness and builds up a classifiers' to judge whether the transaction is fraudulent. Each user's private data is trained offline at the local blockchain node, then the trained model is directly uploaded to the cloud, and the final consensus model is obtained by voting. Due to incorporating blockchain technology, GBDT-APBT demonstrates decentralisation, openness, autonomy, anonymity, and immutability, showing its ability to satisfying the demand for an effective and beneficial anti-counterfeiting system, with high performance and effectiveness in detecting fraud information. Experiments show that compared with other methods, GBDT-APBT offers a promising approach to the security of credit card transactions with reference to the detection accuracy.
With the wide application of Internet of Things and blockchain, research on smart contracts has received increased attention, and security threat detection for smart contracts is one of the main focuses. This article first introduces the common security vulnerabilities in blockchain smart contracts, and then classifies the vulnerabilities detection tools for smart contracts into six categories according to the different detection methods: 1) formal verification method; 2) symbol execution method; 3) fuzzy testing method; 4) intermediate representation method; 5) stain analysis method; and 6) deep learning method. We test 27 detection tools and analyze them from several perspectives, including the capability of detecting a smart contract version. Finally, it is concluded that most of the current vulnerability detection tools can only detect vulnerabilities in a single and old version of smart contracts. Although the deep learning method detects fewer types of smart contract vulnerabilities, it has higher detection accuracy and efficiency. Therefore, the combination of static detection methods, such as deep learning method and dynamic detection methods, including the fuzzy testing method to detect more types of vulnerabilities in multi-version smart contracts to achieve higher accuracy is a direction worthy of research in the future.
Purpose- The aim of this study is to reveal the publications that shed light on the dark side of the cryptocurrency markets with a systematic approach. Methodology- For this purpose, 369 publications in the Scopus database between 2014-2022 were determined as samples. In the publications provided by the database, the keywords "cryptocurrency" and "fraud", "scam", "phishing", "ponzi", "crime" were scanned over the publication title, abstract and keywords, and an integrated bibliometric analysis was employed. The R program was used in the analysis, and the "Biblioshiny" application in the RStudio program was employed to visualize the findings. Findings- The analysis reveals that the number of publications, the number of citations and the interest in the field have increased especially in recent years. The rate of increase in the number of publications in the field and the fact that most of these publications are at the stage of notification have shown that the field is an important developing field. The most intense interest in the field has been shown from universities in China. On the other hand, it was seen that the most interest in the field was from computer sciences and the interest of journals in the field of finance remained weak. It has been determined that the topics that may attract attention in the future are digital forensics, digital assets, fraudulent cryptocurrencies, corruption prevention, mining and cyber attacks. Conclusion- The study reveals the evolution of the dark side of cryptocurrency markets in academic research. The findings provide researchers interested in the field with the opportunity to explore themes and issues that may be on the agenda in the future. Keywords: Cryptocurrency, fraud, ponzi, crime, bibliometric analysis JEL Codes: G11, G19
As smart contracts process digital assets, their security is essential for blockchain applications. Many approaches have been proposed to detect smart contract vulnerabilities. Studies show that few of the reported vulnerabilities are exploited and hypothesize that many of the reported vulnerabilities are false positives. However, no follow-up study is performed to confirm the hypothesis and understand why the reported vulnerabilities are not exploited. In this study, we first collect 136,969 unique real-world smart contracts and analyze them using four vulnerability detectors, namely Oyente, SmartCheck, Slither, and SolDetector. Then, we apply Strauss’ grounded theory approach to manually analyze the source code of the smart contracts reported as vulnerable to recognizing false positives and understand the reasons for false results. In addition, we analyze the transaction logs of the smart contracts reported as vulnerable to identifying and understanding their exploitations. Our results show that 75.37% of the 4,364 smart contracts reported as vulnerable are false positives, and eleven reasons are causing the false positives. After analyzing the 4,106,134 transaction logs of the contracts reported as vulnerable, we find that vulnerabilities of only 67 (0.015%) of the contracts have been exploited in history. We also identify six reasons for demotivating and preventing the attackers from exploiting the vulnerabilities. Our results reveal that state-of-the-art smart contract vulnerability detectors primarily treat the smart contracts as yet another application developed using Object Oriented (OO) languages when analyzing and reporting the smart contract vulnerabilities. Without considering the specific design principles of the Solidity programming language and the characteristics of smart contracts’ application scenarios and execution environments, many of the reported vulnerabilities are not exploitable or not cost-effective to be exploited by adversaries.
Hanna Kim, Jian Cui, Eugene Jang, Chanhee Lee · 7 authors
As Non-Fungible Tokens (NFTs) continue to grow in popularity, NFT users have become targets of phishing attacks by cybercriminals, called \textit{NFT drainers}. Over the last year, \$100 million worth of NFTs were stolen by drainers, and their presence remains a serious threat to the NFT trading space. However, no work has yet comprehensively investigated the behaviors of drainers in the NFT ecosystem. In this paper, we present the first study on the trading behavior of NFT drainers and introduce the first dedicated NFT drainer detection system. We collect 127M NFT transaction data from the Ethereum blockchain and 1,135 drainer accounts from five sources for the year 2022. We find that drainers exhibit significantly different transactional and social contexts from those of regular users. With these insights, we design \textit{DRAINCLoG}, an automatic drainer detection system utilizing Graph Neural Networks. This system effectively captures the multifaceted web of interactions within the NFT space through two distinct graphs: the NFT-User graph for transaction contexts and the User graph for social contexts. Evaluations using real-world NFT transaction data underscore the robustness and precision of our model. Additionally, we analyze the security of \textit{DRAINCLoG} under a wide variety of evasion attacks.
Minh Hoang Nguyen, Phuong Duy Huynh, Son Hoang Dau, Xiaodong Li
The rapid development of blockchain and cryptocurrency in the past decade has created a huge demand for digital trading platforms. Popular decentralised exchanges (DEXs) such as Uniswap and PancakeSwap were created to address this market gap, facilitating cryptocurrency exchange without intermediaries and hence eliminating security and privacy issues associated with traditional centralised platforms. This, however, due to lack of regulation, results in the emergence of a host of damaging investment fraudulent schemes, including Ponzi, honey pot, pump-and-dump, and rug-pull.In this study, we aim to investigate the problem of detecting rug-pull on Uniswap using supervised learning. We aggregate a list of 23 features and propose the use of a hybrid feature selection technique to find the most relevant features for rug-pull. The classifier, using this refined set of features, outperforms the classifier in the previous studies and achieves an f1-score of 99%, a precision of 97% on non-malicious tokens, and a recall of 99% on malicious tokens. Additionally, we show that the XGBoost classifier, built using these proposed features, can distinguish scam tokens and newly listed tokens, which are often harder to differentiate as they have similar characteristics, and also propose a validation method.
Non-fungible token (NFT) is a variant of tokens that can be used to represent unique and valuable digital items. NFT can prove true ownership of virtual assets in game spaces, allowing it to be utilized in computer games. It is an emerging field that attracts a great deal of academic and professional interest. Nonetheless, there are critics of the NFT game, and some may compare it to a Ponzi scheme. The purpose of this study is to identify the recent research trends in the field of NFT game research. We discovered 23 NFT game articles by searching the SSCI/SCI Expanded, Scopus, and IEEE Explore Library databases. The study summarizes these 23 articles in order to reveal the major issues addressed in the literature. Recent research on social media influencers yields fundamental insights from these findings.
Sayak Saha Roy, Dipanjan Das, Priyanka Bose, Christopher Kruegel · 6 authors
The rapid growth in popularity and hype surrounding digital assets such as art, video, and music in the form of non-fungible tokens (NFTs) has made them a lucrative investment opportunity, with NFT-based sales surpassing $25B in 2021 alone. However, the volatility and general lack of technical understanding of the NFT ecosystem have led to the spread of various scams. The success of an NFT heavily depends on its online virality. As a result, creators use dedicated promotion services to drive engagement to their projects on social media websites, such as Twitter. However, these services are also utilized by scammers to promote fraudulent projects that attempt to steal users' cryptocurrency assets, thus posing a major threat to the ecosystem of NFT sales. In this paper, we conduct a longitudinal study of 439 promotion services (accounts) on Twitter that have collectively promoted 823 unique NFT projects through giveaway competitions over a period of two months. Our findings reveal that more than 36% of these projects were fraudulent, comprising of phishing, rug pull, and pre-mint scams. We also found that a majority of accounts engaging with these promotions (including those for fraudulent NFT projects) are bots that artificially inflate the popularity of the fraudulent NFT collections by increasing their likes, followers, and retweet counts. This manipulation results in significant engagement from real users, who then invest in these scams. We also identify several shortcomings in existing anti-scam measures, such as blocklists, browser protection tools, and domain hosting services, in detecting NFT-based scams. We utilize our findings to develop and open-source a machine learning classifier tool that was able to proactively detect 382 new fraudulent NFT projects on Twitter.
Crime is an unlawful act and is punished by the authority, in order to prove crime, evidence is necessary. Evidence obtained from crime scene is crucial as it act as proof of crime, Digitalization of evidences is need of hour. During the entire process of investigation heterogenous format of data is generated and integrity of the sensitive data has to be maintained as sensitive data passes through the various levels of intermediaries forming Chain of Evidences (CoE). Evidence needs to be tramper proof and should be protected from any kind of alterations. In order to build strong system with immutability, integrity, and legitimacy features blockchain technology is more suitable. The digital evidence can be transferred in a transparent way between the parties involved without any central authority using blockchain technology. This paper focuses on how blockchain based solutions can help in building a strong secure system. The system is implemented using Ethereum platform to achieve integrity, immutability transparency as well as tampering can be identified by any one at any time.
Allen Xavier Peter, K. Manoj, Priyan Malarvizhi Kumar
A credit card is a convenient and widely recognized method of making cashless transactions both online and offline. One of the most significant benefits of using a credit card rather than a debit card is that it allows you to borrow money to pay for your transactions. As well as the majority of online fraud occurs during a card or online transaction when a user attempts to buy something or move money. Nowadays lots of technology introduced for secured money transactions, that's blockchain technology. The blockchain has the potential to evolve into a distributed ledger, offering a revolutionary new form of trustworthy third-party authentication. Because of the long history of credit card systems, it is easier to understand and security has always been triggered by a process of delegating risk to third parties. Blockchain technology has the potential to avoid these types of losses from occurring in the first place. This study examines how Blockchain technology may be applied, how it might br made safe, and how it might be used to reduce the danger of credit card data being compromised. Additionally, this article identifies and discusses a mechanism that may be created utilizing current technologies, such as multiple identification, SR4S randomized OTP (One Time Password), and biometric tools, to avoid the loss of credit cards.
Ransomware attacks are one of the most dangerous related crimes in the coin market. To increase the challenge of fighting the attack, early detection of ransomware seems necessary. In this article, we propose a high-performance Bitcoin transaction predictive system that investigates Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks into malicious or benign transactions. The proposed approach makes use of three supervised machine learning methods to learn the distinctive patterns in Bitcoin payment transactions, namely, logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost). We evaluate these ML-based predictive models on the BitcoinHeist ransomware dataset in terms of classification accuracy and other evaluation measures such as confusion matrix, recall, and F1-score. It turned out that the experimental results recorded by the XGBoost model achieved an accuracy of 99.08%. As a result, the resulting model accuracy is higher than many recent state-of-the-art models developed to detect ransomware payments in Bitcoin transactions.
With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology. Meanwhile, various vulnerabilities in smart contracts have been exploited by attackers to steal cryptocurrencies worth millions of dollars. The automatic detection of smart contract vulnerabilities therefore is an essential research problem. Existing solutions to this problem particularly rely on human experts to define features or different rules to detect vulnerabilities. However, this often causes many vulnerabilities to be ignored, and they are inefficient in detecting new vulnerabilities. In this study, to overcome such challenges, we propose a framework to automatically detect vulnerabilities in smart contracts on the blockchain. More specifically, first, we utilize novel feature vector generation techniques from bytecode of smart contract as source code is rarely publicly available. These feature vectors are then analyzed using our innovative metric learning-based Deep Neural Networks (DNNs) to produce detection results. The framework’s predictions are further refined through a voting mechanism to achieve consensus. We conduct comprehensive experiments on large-scale benchmarks, and the quantitative results demonstrate the effectiveness and efficiency of our approach.