Phan The Duy, Nghi Hoang Khoa, Nguyen Huu Quyen, Le Cong Trinh · 7 authors
This paper presents VulnSense framework, a comprehensive approach to efficiently detect vulnerabilities in Ethereum smart contracts using a multimodal learning approach on graph-based and natural language processing (NLP) models. Our proposed framework combines three types of features from smart contracts comprising source code, opcode sequences, and control flow graph (CFG) extracted from bytecode. We employ Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Long Short-Term Memory (BiLSTM) and Graph Neural Network (GNN) models to extract and analyze these features. The final layer of our multimodal approach consists of a fully connected layer used to predict vulnerabilities in Ethereum smart contracts. Addressing limitations of existing vulnerability detection methods relying on single-feature or single-model deep learning techniques, our method surpasses accuracy and effectiveness constraints. We assess VulnSense using a collection of 1.769 smart contracts derived from the combination of three datasets: Curated, SolidiFI-Benchmark, and Smartbugs Wild. We then make a comparison with various unimodal and multimodal learning techniques contributed by GNN, BiLSTM and BERT architectures. The experimental outcomes demonstrate the superior performance of our proposed approach, achieving an average accuracy of 77.96\% across all three categories of vulnerable smart contracts.
This article discusses the reasons behind the Chinese government's conservative attitude toward cryptocurrencies, despite the rapid growth of the cryptocurrency market in China. Cryptocurrencies have become popular in finance because of their advantages in privacy, permanence, and scalability. However, they also pose challenges, such as regulatory un-certainties and security risks. China has banned financial institutions from facilitating bitcoin transactions and imposed restrictions on cryptocurrencies. The article suggests that China's attitude towards cryptocurrencies may be due to their defects. Clear regulations and laws are needed to normalize transactions and issuance and mitigate policy risks. The article also provides some possible solutions based on the defects of cryptocurrencies.
Multiple works have leveraged the public Bitcoin ledger to estimate the revenue cybercriminals obtain from their victims. Estimations focusing on the same target often do not agree, due to the use of different methodologies, seed addresses, and time periods. These factors make it challenging to understand the impact of their methodological differences. Furthermore, they underestimate the revenue due to the (lack of) coverage on the target's payment addresses, but how large this impact remains unknown. In this work, we perform the first systematic analysis on the estimation of cybercrime bitcoin revenue. We implement a tool that can replicate the different estimation methodologies. Using our tool we can quantify, in a controlled setting, the impact of the different methodology steps. In contrast to what is widely believed, we show that the revenue is not always underestimated. There exist methodologies that can introduce huge overestimation. We collect 30,424 payment addresses and use them to compare the financial impact of 6 cybercrimes (ransomware, clippers, sextortion, Ponzi schemes, giveaway scams, exchange scams) and of 141 cybercriminal groups. We observe that the popular multi-input clustering fails to discover addresses for 40% of groups. We quantify, for the first time, the impact of the (lack of) coverage on the estimation. For this, we propose two techniques to achieve high coverage, possibly nearly complete, on the DeadBolt server ransomware. Our expanded coverage enables estimating DeadBolt's revenue at $2.47M, 39 times higher than the estimation using two popular Internet scan engines.
Jiachi Chen, Jiang Hu, Xin Xia, David Lo · 7 authors
Decentralized Finance (DeFi) uses blockchain technologies to transform traditional financial activities into\ndecentralized platforms that run without intermediaries and centralized institutions. Smart contracts are\nprograms that run on the blockchain, and by utilizing smart contracts, developers can more easily develop\nDeFi applications. Some key features of smart contracts – self-executed and immutability – ensure the\ntrustworthiness, transparency and efficiency of DeFi applications, and have led to a fast-growing DeFi market.\nHowever, misbehaving developers can add traps or backdoor code snippets to a smart contract, which are\nhard for contract users to discover. We call these code snippets in a DeFi smart contract as “DeFi Contract\nTraps" (DCTs). In this paper, we identify five DeFi contract traps and introduce their behaviors, describe\nhow attackers use them to make unfair profits, and analyse their prevalence in the Ethereum platform. We\npropose a symbolic execution tool, DeFiDefender, to detect such traps and use a manually labeled small-scale\ndataset that consists of 700 smart contracts to evaluate it. Our results show that our tool is not only highly\neffective but also highly efficient. DeFiDefender only needs 0.48s to analyze one DeFi smart contract and\nobtains a high average accuracy (98.17%), precision (99.74%), and recall (89.24%). Among the five DeFi contract\ntraps introduced in this paper, four of them can be detected through contract bytecode without the need for\nsource code. We also apply DeFiDefender to a large-scale dataset that consists of 20,679 real DeFi related\nEthereum smart contracts. We found that 52.13% of these DeFi smart contracts contain at least one contract\ntrap. Although a smart contract that contains contract traps is not necessarily malicious, our finding suggests\nthat DeFi related contracts have many centralized issues in a zero-trust environment and in the absence of a\ntrusted part
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.
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
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.
Matteo Loporchio, Anna Bernasconi, Damiano Di Francesco Maesa, Laura Ricci
Abstract In the Bitcoin protocol, dust refers to small amounts of currency that are lower than the fee required to spend them in a transaction. Although “economically irrational”, dust is commonly used for achieving unconventional side effects, rather than exchanging value. For instance, dust might be linked to on-chain services or to malicious activity, such as dust attacks attempting to break users’ pseudonymity. To study this phenomenon, this paper presents an in depth analysis of Bitcoin transactions involving dust, showing how dust is created and consumed. We identify the top dust creators and consumers and discuss how consumption has evolved over time. Finally, we use the data to identify transactions suspected of being part of dust attacks and quantify their impact on address deanonymization. Our results show that dust is mainly related to on-chain betting services. Transactions likely to be part of dust attacks are a minority of dust creating transactions but, despite their relatively low number, they have a disproportionately high effect on helping attackers to break address pseudonymity.
The research paper discusses governance implications, benefits, and challenges of applying internationally recognized internal auditing standards to the newly emerging Decentralized Autonomous Organizations (DAOs) that have quickly gained traction in the past years and are currently totaling market capitalizations of more than USD 20 billion globally. It is analyzed how standards established for traditional centralized organizations are compatible with a decentralized, often anonymous organization that makes decisions democratically based on majority votes while most operations are conducted autonomously subject to pre-defined self-executing smart contracts. After the technological attributes of blockchains, smart contracts, DAOs and other general considerations are determined, each IIA standard is applied separately and results are drawn from a qualitative analysis. The publication contains the major conclusions from a literature analysis followed by a summary of conceptual obstacles to complying with the standards in case of selecting a DAO as an organizational form which could make their overall legality impossible in a context where the implementation of an IA function is mandatory. Additionally, it is summarized how choosing a DAO can contribute and/or challenge compliance with the standards while giving a glimpse into what internal auditing could look like in the future.
Permissionless blockchains promise to be resilient against censorship by a single entity. This suggests that deterministic rules, and not third-party actors, are responsible for deciding if a transaction is appended to the blockchain or not. In 2022, the U.S. Office of Foreign Assets Control (OFAC) sanctioned a Bitcoin mixer and an Ethereum application, putting the neutrality of permissionless blockchains to the test. In this paper, we formalize quantify and analyze the security impact of blockchain censorship. We start by defining censorship, followed by a quantitative assessment of current censorship practices. We find that 46% of Ethereum blocks were made by censoring actors that intend to comply with OFAC sanctions, indicating the significant impact of OFAC sanctions on the neutrality of public blockchains. We further uncover that censorship not only impacts neutrality, but also security. We show how after Ethereum's move to Proof-of-Stake (PoS) and adoption of Proposer-Builder Separation (PBS) the inclusion of censored transactions was delayed by an average of 85%. Inclusion delays compromise a transaction's security by, e.g., strengthening a sandwich adversary. Finally we prove a fundamental limitation of PoS and Proof-of-Work (PoW) protocols against censorship resilience.
Blockchain provides the unique and accountable channel for financial forensics by mining its open and immutable transaction data. A recent surge has been witnessed by training machine learning models with cryptocurrency transaction data for anomaly detection, such as money laundering and other fraudulent activities. This paper presents a holistic applied data science approach to fraud detection in the Bitcoin network with two original contributions. First, we contribute the Elliptic++dataset, which extends the Elliptic transaction dataset to include over 822k Bitcoin wallet addresses (nodes), each with 56 features, and 1.27M temporal interactions. This enables both the detection of fraudulent transactions and the detection of illicit addresses (actors) in the Bitcoin network by leveraging four types of graph data: (i) the transaction-to-transaction graph, representing the money flow in the Bitcoin network, (ii) the address-to-address interaction graph, capturing the types of transaction flows between Bitcoin addresses, (iii) the address-transaction graph, representing the bi-directional money flow between addresses and transactions (BTC flow from input address to one or more transactions and BTC flow from a transaction to one or more output addresses), and (iv) the user entity graph, capturing clusters of Bitcoin addresses representing unique Bitcoin users. Second, we perform fraud detection tasks on all four graphs by using diverse machine learning algorithms. We show that adding enhanced features from the address-to-address and the address-transaction graphs not only assists in effectively detecting both illicit transactions and illicit addresses, but also assists in gaining in-depth understanding of the root cause of money laundering vulnerabilities in cryptocurrency transactions and the strategies for fraud detection and prevention. The Elliptic++ dataset is released at https://www.github.com/git-disl/EllipticPlusPlus.
With the overall momentum of the blockchain industry, crypto-based crimes are becoming more and more prevalent. After committing a crime, the main goal of cybercriminals is to obfuscate the source of the illicit funds in order to convert them into cash and get away with it. Many studies have analyzed money laundering in the field of the traditional financial sector and blockchain-based Bitcoin. But so far, little is known about the characteristics of crypto money laundering in the blockchain-based Web3 ecosystem. To fill this gap, and considering that Ethereum is the largest platform on Web3, in this paper, we systematically study the behavioral characteristics and economic impact of money laundering accounts through the lenses of Ethereum heists. Based on a very small number of tagged accounts of exchange hackers, DeFi exploiters, and scammers, we mine untagged money laundering groups through heuristic transaction tracking methods, to carve out a full picture of security incidents. By analyzing account characteristics and transaction networks, we obtain many interesting findings about crypto money laundering in Web3, observing the escalating money laundering methods such as creating counterfeit tokens and masquerading as speculators. Finally, based on these findings we provide inspiration for anti-money laundering to promote the healthy development of the Web3 ecosystem.