Modern advancement in technological field has brought changes into the banking, financial, and capital market in India. Blockchain is a new-age transaction mode that has come into the radar after scams had raised concern pertaining to the regulatory mechanism of blockchain in the legal system of India. Cryptocurrencies that are used as a mode of transfer or payment in India is not regulated by any of the centralized authorities; nor there are any rules, regulations, laws, or guidelines provided for settling of any of the disputes between parties who are dealing the cryptocurrency. They are dealing with their own risk as investors, as there is no involvement of banking or capital market regulators like Reserve Bank of India (RBI) or Securities and Exchange Board of India (SEBI). Cryptocurrency is used anonymously to conduct transactions globally between account holders.
Bitcoin has received a lot of attention as a cryptocurrency in recent years. The paper focuses on determining if Bitcoin would replace world currency in the future, which compares Bitcoin with the US dollar and gold. The article considers qualitative analysis to discuss the bitcoin's characteristics based on Karl Marx's five different categories in his book Das Kapital: measure value, means of circulation, means of hoarding, means of payment, and universal currency. The paper presents the advantages and disadvantages related to Bitcoin compared to the US dollar and gold. The article finds that Bitcoin is more secure in saving and privacy. Also, it wouldn't be affected by inflation. However, disadvantages are also present when applying Bitcoin in the market. The volatility and value of storing would be a challenge to solve. The lack of population using it and the limitations of transactions would also be problems. The research illustrates that Bitcoin can't replace world currency in the short term, but in the long term, Bitcoin would be the mainstream currency to use in real life due to the performance of people, governments, and the world economy. The article is the first research to utilize Marx's theory to analyze whether Bitcoin could replace world currency in the existing literature. This paper recommends that governments of all countries establish a unified regulatory policy and security mechanism on a global scale to ensure the legitimacy, stability, and security of Bitcoin so that Bitcoin can truly become a world currency.
Universidad Autónoma Metropolitana, Carlos Ortega-Laurel
In this work a guide is proposed, introducing the idea of alternative implementation of blockchain technology to the popularized use as a currency. As an axis of the guide, the use and support of non-fungible token technology (NFT) for the Public Property Registry (RPP) is presented, as an example, which allows us to conceive the different technological and non-technological aspects that the use of this technology would involve in contexts such as the one explained. The research of the technological fund, the institutional-legal fund, and the techno-institutional-legal is exposed, this to holistically foresee the implications that would have the use of the registry transactions of the RPP through a blockchain supported by NFT tokens. Subsequently, and based on the set of variables, the interdisciplinary research problem is externalized, while summarizing the proposal and discussing its derivations and findings. Finally, it concludes by giving an account of the main challenges and guidelines in which any organization or institution with characteristics analogous to the RPP that seeks a digital transformation and innovation in the near future will have to work.
The novel use of distributed ledger technology (DLT) in the financial sector poses considerable regulatory challenges, such as anonymity, technology neutrality, interconnectedness within the market of virtual assets, as well as with the traditional financial system and new legal risks to regulators around the globe. At the same time, the novelty of DLT for financial uses embodies also significant potential benefits to the financial sector, like innovation, inclusion, and competition. This chapter analyses these challenges and opportunities in detail and subsequently reviews possible regulatory responses at the current stage of virtual asset revolution and financial services innovation. Given the ongoing rapid development in the DLT financial services sphere, the analysis identifies the risk-based regulatory approach as potentially the most universal approach for national/regional regulators with advantages of high flexibility and resource efficiency.
Cryptocurrencies have become an increasingly popular means of conducting financial transactions globally, and South African banking institutions have not been immune to this trend. However, the pseudonymous nature of cryptocurrency transactions has made it an attractive tool for money laundering activities. In response, there is a growing need for South African regulators to establish a legal framework to regulate the use of cryptocurrency to combat money laundering crimes by banking institutions. While the recent amendments to the Financial Intelligence Centre Act 38 of 2001 (as amended) regarding cryptocurrencies are commendable, it is not without deficiencies. The purpose of this article is threefold. First, it examines the current state of cryptocurrency regulation in South Africa. Second, it explores the vulnerabilities that expose the banking system to money laundering using cryptocurrencies. Third, it highlights the need for further development and implementation of regulatory measures to address vulnerabilities identified in this article. This article argues that the current lack of a comprehensive regulatory framework for cryptocurrencies in South Africa leaves the banking system open to potential abuse. The article suggests that South African regulators should focus on three key areas to combat money laundering activities related to cryptocurrency. First, regulatory measures should be implemented to identify and verify the identities of cryptocurrency traders and investors. Second, measures should be put in place to monitor the flow of cryptocurrency transactions and detect suspicious activities. Third, the digital wallets of crypto users should be managed by South African banking institutions.
The purpose of this paper is to examine the role of cryptocurrency in facilitating money laundering and identify different methods and services that send funds through numerous addresses or businesses to obscure their origins using cryptocurrency. The methodology for conducting this research is qualitative. A literature review that involves a systematic and rigorous approach to identifying, analyzing, and synthesizing existing research on the use of cryptocurrency as a money laundering instrument has been taken into consideration. We identified in the first selection more than 150 research papers published between 2002 and 2021. Our results show that cryptocurrency is used in money laundering schemes, including the purchase of cryptocurrencies by criminal networks using illicit proceeds and the use of cryptocurrencies to transfer funds. The biggest issue facing virtual currency currently is that the same attributes that attract legitimate users, such as anonymity, as well as speed and global reach, also attract criminals. Money laundering has had devastating social implications for societies. Our research helps to focus attention on the problems of using cryptocurrency in money laundering practices and possible interventions by the authorities in the form of regulation
Digital currencies have emerged as a new platform for money laundering as the first blockchain platform to support smart contracts, and the number of transaction records for Bitcoin has expanded substantially in recent years. Due to privacy services such as tumblers, which conceal the identities of payers and payees, pseudonymous digital currencies are frequently referred to as anonymous address and untraceable. A large proportion of accounts participating in Bitcoin transactions are engaging in illegal activity. According to the digital ledger, a transaction was transferred from one of many potential payers to one of many possible payees. As a consequence, it is vital to have a dependable system for classifying accounts and keeping track of the transactions linked with each account. We utilized machine learning techniques to apply multi-class classification methods to a Bitcoin transaction record obtained from kaggle.com in order to improve the model’s accuracy. Experiment results show that ML algorithms like logistic regression, random forests, and multilayer perceptron’s with the macro-average F1 score for bitcoin tumblers outperform state-of-the-art approaches for identifying unknown digital currency accounts, with the RF model outperforming LR and MLP by 97% accuracy.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Antulio Rosales, Eva van Roekel, Peter Howson, Coco Kanters
This article examines how cryptocurrencies are increasingly entangled with crises in Latin American political discourse and everyday economic life. In an effort of interdisciplinary integration, combining human geography with political economy and cultural anthropology, we critically assess the linkages between cryptocurrency, economic crisis and forms of political and economic precarity and exploitation. Drawing on experiences in Latin America, mostly on the cases of El Salvador and Venezuela, we explore how cryptocurrencies have rapidly emerged and expanded during periods of economic and political crises. We ground this discussion on social theories of money and critical analysis of blockchain and cryptocurrencies that question the apolitical assumptions of these apparent “trustless” infrastructures. The article contends that cryptocurrencies have the capacity to create potential niches for makeshift economic survival, speculation and quick profit, while at the same time reproducing historical conditions of vulnerability, inequality and ‘crypto-colonialism’. Though cryptocurrencies are surrounded by stories of freedom and decentralised community control, our ethnographic data on El Salvador and Venezuela suggest they often rely on free market fundamentalism and conditions of political corruption by authoritarian state-backed elites.
Decentralized Finance (DeFi) platforms are often governed by Decentralized Autonomous Organizations (DAOs) which are implemented via governance protocols. Governance tokens are distributed to users of the platform, granting them voting rights in the platform's governance protocol. Many DeFi platforms have already been subject to attacks resulting in the loss of millions of dollars in user funds. In this paper we show that governance tokens are often not used as intended and may be harmful to the security of DeFi platforms. We show that (1) users often do not use governance tokens to vote, (2) that voting rates are negatively correlated to gas prices, (3) voting is very centralized. We explore vulnerabilities in the design of DeFi platform's governance protocols and analyze different governance attacks, focusing on the transferable nature of voting rights via governance tokens. Following the movement and holdings of governance tokens, we show they are often used to perform a single action and then sold off. We present evidence of DeFi platforms using other platforms' governance protocols to promote their own agenda at the expense of the host platform.
Central banks worldwide are developing, piloting and launching new central bank digital currencies (CBDCs). As the hub for the central banking community, the Bank for International Settlements (BIS) promotes a curiously botanical CBDC imaginary. From ‘money flowers’ to ‘tree trunks’ and a ‘strong canopy’, This helps to naturalize CBDC without clarifying its sociopolitical implications or envisioned monetary future, such as geopolitical tensions and financial fragmentation, new modes of financial interaction or the strengthened role of central banks. While omitting the paradoxes and ambivalences of CBDC, the imaginary of the BIS structures the enfolding discourses and allows the bank to function as a think tank for financial policy‐making.
In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions.
The number of money laundering crimes for Ethereum and the amount involved have grown exponentially in recent years. However, previous studies related to anomaly detection for Ethereum usually consider multiple types of financial crimes as a whole, ignoring the apparent differences between money laundering and other malicious activities and lacking a more granular detection targeting money laundering. In this paper, for the first time, we propose an improved graph embedding algorithm specifically for money laundering detection called GTN2vec. By mining Ethereum transaction records, the algorithm comprehensively considers the behavioral patterns of money launderers and structural information of transaction networks and can automatically extract features of money laundering addresses. Specifically, we fuse the gas price and timestamp from the transaction records into a new weight and set appropriate return and exploration parameters to modulate the sampling tendency of random walk to characterize the money laundering nodes. We construct the dataset using real Ethereum data and evaluate the effectiveness of GTN2vec on the dataset by various classifiers such as random forest. The experimental results show that GTN2vec can accurately and effectively extract money laundering account features and significantly outperform other advanced graph embedding methods.
Abstract: Know your client or simply KYC, is a process utilized by businesses and financial institutions to identify their clients and evaluate any potential risks associated with illegal intentions and unethical behavior. The term KYC often refers to bank regulations and anti-money laundering regulations aimed at governing these activities. Due to concerns over bribery and unethical behavior, companies of all sizes are required to implement KYC to ensure their agents, consultants, and distributors comply with anti-bribery regulations. Despite the use of traditional KYC systems, there are limitations to their effectiveness. To address these limitations, a proposed system has been developed that uses the immutable nature of Distributed Ledger Technology (DLT) to create a tamper-proof system. This system enables customers and financial institutions to verify and record KYC documents on the DLT, providing greater efficiency, cost reduction, improved customer experience, and end-to-end transparency in integrating customer documents into the bank's database. Additionally, this system eliminates the need for repeated KYC checks performed by banks through the creation of a secure and common blockchain database. The blockchain's secure nature ensures that unauthorized changes to the data are immediately invalidated and the use of a proof-of-reputation concept makes the verification process more robust.
Abstract This work considers a combinatorial optimization problem in graphs, the nilcatenation problem, and investigates its potential application for detecting money laundering activities in cryptocurrency networks. The nilcatenation problem consists of finding a set of arcs that can be removed from an arc‐weighted directed graph without changing the balance of any vertex. The balance of a vertex is defined as the difference between the sum of the weights of outgoing and incoming arcs. We propose a 0/1 integer linear programming formulation and a local branching algorithm. The approaches are computationally evaluated and compared using three sets of test instances, two of them generated from Bitcoin's testnet and mainnet networks. An experiment on the testnet showed that it is possible to retrieve a nilcatenation artificially introduced with fake bitcoin transactions. Experiments on the mainnet showed that it is possible to find large nilcatenations, possibly indicating money laundering activities.
Rafael Ramos Tubino, Rémy Cazabet, Natkamon Tovanich, Céline Robardet
We study the real economic activity in the Bitcoin blockchain that involves transactions from/to retail users rather than between organizations such as marketplaces, exchanges, or other services. We first introduce a heuristic method to classify Bitcoin players into three main categories: Frequent Receivers (FR), Neighbors of FR, and Others. We show that most real transactions involve Frequent Receivers, representing a small fraction of the total value exchanged according to the blockchain, but a significant fraction of all payments, raising concerns about the centralization of the Bitcoin ecosystem. We also conduct a weekly pattern analysis of activity, providing insights into the geographical location of Bitcoin users and allowing us to quantify the bias of a well-known dataset for actor identification.
Abstract The rise of Non-Fungible Tokens (NFTs) is beginning to revolutionize the digital world thanks to the unique property of these tokens. Indeed, they can represent the ownership of physical or digital assets. They are implemented using smart contracts, therefore if the code of the smart contract contains bugs, an attacker can exploit its vulnerabilities to perform an attack called sleepminting. Sleepminting consists of transferring NFTs owned by an address, without the owner’s consent. In this paper, we provide a detailed analysis of the sleepminting attack and, thanks to the insights gained, we propose a prevention system to reduce the number of sleepminting attacks. Our prevention system is based on analysing the transactions included in new blocks, detecting those that are related to sleepminting attacks and keeping track of the addresses that are involved in these transactions. A dictionary-like data structure can be used to keep track of the addresses involved, where the key is the address and the value acts as a counter for the number of times the address is involved in sleepminting. With this information, block-creating nodes can add another verification step before adding a transaction to a block, which consists of blocking transactions when the addresses involved appear in sleepminting attacks a number of times greater than a threshold. The evaluation shows that sleepminting is a relevant phenomenon, and now it involves NFT transfers rather than NFT minting. Our proposed prevention system is able to block up to 87% of attacks.
India is experiencing a sharp rise in criminal activity. This is a serious problem, as many of these crimes go unreported. Although there is an online platform for the police to store First Information Reports (FIR) and Non-Cognizable Reports (NCR), most FIRs are still written by hand. This is inefficient and can lead to errors. Additionally, the complainant must typically be at the police station to report a cognizable offense. This can be inconvenient and time-consuming, especially for victims who live in rural areas. In 2009, the Crime and Criminal Tracking Network and Systems (CCTNS) were launched as an efficient e-governance system. This system has helped to improve the reporting of crimes, but it is still a centralized system. This means that it is vulnerable to cyberattacks and can be easily shut down by a single point of failure. Therefore, a fully decentralized system is required to ensure no single point of failure and that complaints are handled safely and securely to prevent unauthorized access. This paper proposes a blockchain-based solution called BlockFIR to manage complaints against cognizable and non-cognizable offenses. Using this system, complaints can be registered by users. The police stations will be able to see complaints registered in their jurisdiction, register FIRs/NCRs accordingly, and take action on them. Through a prototype implementation using Go-Ethereum (Geth), smart contracts, and Django web server, we demonstrate the practical use of BlockFIR. We show that our system can be easily used by users, police personnel, and Higher Authorities to improve the current systems in India.
Smart contracts have emerged as one of the most successful applications in the blockchain domain, playing a significant role in various blockchain ecosystems. Inspired by smart contracts, a multitude of cryptographic assets have been created. To standardize these assets, industry standards such as ERC20 (Ethereum Request for Comments 20), ERC721, and ERC1155 have been proposed. In recent years, smart contracts have frequently fallen victim to attacks. Honeypot contracts, disguised as ERC20-compliant tokens, are widely prevalent on the blockchain, enticing victims to make purchases. Such malicious smart contracts exhibiting deceptive behavior are collectively referred to as honeypot tokens. This paper focuses on ERC20-compliant smart contracts and defines six common types of honeypot issues. Building upon advancements in smart contract vulnerability detection, we propose an enhanced symbolic execution-based detection tool called Honeytoken-Detector. We conduct experiments on both contracts known to have similar issues and actual token contracts from the real world. The experimental results demonstrate the effectiveness of our tool in identifying vulnerabilities.
Centralized cryptocurrency exchanges offer users a more convenient platform to trade their digital assets at the cost of reduced control. As a result, when these exchanges suffer interruptions users struggle to access their funds or modify their orders. We investigate 41 events at the popular exchange Bitfinex, and measure the impact these events have on trades, volume, and pricing. We find that the volume to trade ratio increases during events, as fewer traders are moving large amounts of bitcoin. We also find that these interruptions often occur at the same time as arbitrage opportunities, with substantial profit opportunities.
Xun Deng, Zihan Zhao, Sidi Mohamed Beillahi, Han Du · 8 authors
This paper presents FrontDef, a security system to detect and front-run malicious transactions to mitigate financial loss caused by smart contract attacks. FrontDef monitors each transaction in the pending transaction pool to detect potential attacks. For each suspicious transaction, FrontDef analyzes the bytecode of the contract the transaction attempts to interact and assembles a sequence of mimic transactions to replicate the attack strategy. FrontDef then uses the assembled transactions to front- run the suspicious attack transaction to prevent financial loss. Empirical results show that FrontDef can successfully detect and assemble mimic transactions for all of the 24 benchmark cases that includes 21 historical attacks that occurred on Ethereum and Binance Smart Chain (BSC). They also confirm that FrontDef can process up to 1230 transactions per second, which currently is greater than the maximum throughput of Ethereum and BSC.
Vyacheslav Davydov, Alexander Krymov, Deniz Ozmaden, Yaroslav Pashchenko · 6 authors
This paper demonstrates the use of blockchain technology in asset-backed securitization (ABS) and presents Quicktoken, a blockchain platform for ABS. Financial institutions can use Quicktoken to assign a correspondence between initial assets and securities, deploy smart contracts for securities issuance, and store the correspondence between assets and non fungible tokens (NFTs) on the blockchain. Investors can buy, sell, and get dividends upon securities redeem via an Android application, while all transactions are secured by the blockchain. The paper highlights the advantages of blockchain usage in ABS, such as a diversification without a loss of auditability.
Decentralized Finance (DeFi) ecosystem has grown rapidly in the past few years. In the DeFi ecosystem, flash loan is a novel type of uncollateralized loan with nearly negligible lending costs. Malicious attackers can easily borrow a large number of crypto assets, and utilize them to disrupt the price of crypto assets to make a profit. Many flash loan based price manipulation attacks have been reported recently, and caused immense economic losses, e.g., 30 million USD in a single attack. In this paper, we conduct an empirical study on real-world flash loan based attacks in the past two years and present three attack patterns for price manipulation attacks. Then, we propose an approach, LeiShen, to automatically detect price manipulation attacks with asset transfers. We evaluate LeiShen on the first 14,500,000 blocks in Ethereum, and detect 180 attacks with a precision of 78.9%. Among our newly-found attacks, the severest attack has caused a total loss of more than 6.1 million USD.
Lei Yu, Fengjun Zhang, Jiajia Ma, Yang Li · 6 authors
With the development of blockchain technology, security concerns have become increasingly prominent in recent years. Money laundering through blockchain has been found to generate a significant amount of money and has become a serious threat. Towards money laundering detection in Bitcoin, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Graph convolutional network approaches have improved this issue, but they fail to distinguish the importance of surrounding transactions and the structural information of different transactions. To solve above problems, we propose an approach to detect money laundering on blockchain by mining its transaction records, named AEtransGAT. First, we use a novel approach called transGat as an encoder to determine the significance of surrounding transactions by considering the transaction amount values of transaction flows. The original features and the features after graph embedding are combined to address the issue of feature distortion. Second, we deploy the graph autoencoder as the decoder to learn the overall structural information of different transactions, and the concatenated embedding is used to output the classification results as the detector. Finally, we propose our model based on mutual learning in this task which takes the advantages of both transactions classification loss and structure reconstruction loss. We validate the performance of our model on the Elliptic dataset which is the only large open source dataset in Bitcoin anti-money laundering. The results show that our method outperforms current state-of-the-art methods and is linearly scalable.