On January 26, 2018, 58 billion yen ($530 million) worth of a cryptocurrency, NEM, was fraudulently accessed, and was then stolen from the Coincheck Exchange, headquartered in Japan. This hacking incident is unprecedented not only because it was one of the world’s largest cryptocurrency heists, but also because the stolen NEM was sold and money laundered on a crypto market. Three years later, the Metropolitan Police Department, Japan, announced that more than 30 people had been charged for allegedly exchanging NEM cryptocurrency, accounting for one third of the stolen value, for other cryptocurrencies. The hackers have not yet been arrested, and how the stolen NEM was money laundered has not yet been investigated. By resolving two challenges in tracking the stolen NEM and its money laundering, this report shows that there were increasingly larger sales of the stolen NEM over time, and on the last two days of market operation, approximately one third of the stolen NEM was money laundered. Furthermore, this study reveals that there was no pattern in the hour of the day of the sales transactions whereas more sales occurred on Sundays and Mondays. This suggests that the laundering was international and that the stolen NEM was purchased by individuals. These findings emphasize the need for cryptocurrency exchanges to verify the identity of a new user when an account is opened.
Bitcoin, a decentralized cryptocurrency, has not only given rise to a wave of digital innovations but also stirred up considerable controversy. Some have hailed it as the most significant innovation since the Internet, while others have dismissed it as a Ponzi scheme that should be abandoned and forbidden. Regardless of these varying views, this is an innovation in need of scrutiny. In this paper we present a metastory of Bitcoin, based on an interpretative study of 737 news articles between 2011–2019. Through our analysis, we identified five narratives, including The Dark Side, The Bright Side, The Tulip Mania, The Idea, and The Normality. Our analysis demonstrates the interpretive flexibility of technology as influenced by ideologies, and we construct a theoretical model demonstrating media’s role as constructor and conduit. The metastory provides an institutional look at the broader interpretations of digital innovations as well as the multifaceted nature of digital innovations and how their interpretation evolve over time.
Attacks on smart contracts have caused considerable losses to digital assets. Many techniques based on symbolic execution, fuzzing, and static analysis are used to detect contract vulnerabilities. Most of the current analyzers only consider vulnerability detection intra-contract scenarios. However, Ethereum contracts usually interact with others by calling their functions. A bug hidden in a path that depends on information from external contract calls is defined as an inter-contract vulnerability. Failure to deal with this kind of bug can result in potential false negatives and false positives. In this work, we propose Pluto, which supports vulnerability detection in inter-contract scenarios. It first builds an Inter-contract Control Flow Graph (ICFG) to extract semantic information among contract calls. Afterward, it symbolically explores the ICFG and deduces Inter-Contract Path Constraints (ICPC) to check the reachability of execution paths more accurately. Finally, Pluto detects whether there is a vulnerability based on some predefined rules. For evaluation, we compare Pluto with five state-of-the-art tools, including Oyente, Mythril, Securify, ILF, and Clairvoyance on a labeled benchmark and 39,443 real-world Ethereum smart contracts. The result shows that other tools can only detect 10% of the inter-contract vulnerabilities, while Pluto can detect 80% of them on the labeled dataset. Beyond that, Pluto has detected 451 confirmed vulnerabilities on real-world contracts, including 36 vulnerabilities in inter-contract scenarios. Two bugs have been assigned with unique CVE identifiers by the US National Vulnerability Database (NVD). On average, Pluto costs 16.9 seconds to analyze a contract, which is as fast as the state-of-the-art tools.
Building a project starts with innovation and idea but funding is a part that decides the evolution of the project from idea to product. During the last decade funding campaign and project via crowdfunding has become a common theme. With the COVID situation, it has become a necessity for NGOs, campaigns, projects, start-ups to consider the concept of crowdfunding and build seed funds through it, therefore organizations are trying to create a safe, secure, and fraud-proof gateway for people to get funds and as well as provide funds, which is hard in the current time of pandemic and technical advancements. It has become quite easy to fall into a trap and lose your hard-earned funds with cybercrimes as identity fraud, theft of financial data, and internet fraud. This work is an attempt to create a secure, efficient, and viable tool for crowdfunding. The solution proposed has Blockchain integrated to build trust among the funders and those raising these funds, with its characteristics as decentralized, irrefutable, distributed ledgers, consensus, and faster settlement. The proposed model has been built on the smart contract protocol, created for crowdfunding transactions, campaigns for the proposed model was implemented on remix ide, this will create a campaign for those in need of funds and for donors to donate funds to these campaigns. The campaign master has the right to reject or accept requests thus creating fraud and a tamper-proof environment. The model has been subjected to positive & negative unit and integration tests on mocha, the efficiency of the model obtained is at par with existing solutions with an added edge on security via smart contract protocols.
In this article, the author primarily aims to clarify from a technical and terminological point of view several notions relevant for the blockchain ecosystem. In this context, the analysis is focused on notions such as blockchain, Proof of Work or Proof of Stake consensus mechanism, virtual currency, crypto-asset, digital wallet, centralized or decentralized exchange platform, public address, public and private cryptographic key, seed phrase, etc. Beyond these technical and terminological clarifications, the author aims to analyse the European and national regulatory framework, in an attempt to resolve, among other things, different matters such as the difference between virtual currencies, crypto-assets and electronic money or the regulation of exchange service providers and digital wallet providers. Last but not least, the author analyses both the risks and benefits of blockchain technology from a cyber security perspective, as well as several criminal behaviours in regard to virtual currencies or other crypto-assets. In this context, behaviours such as ransomware, cryptojacking, unauthorized transfer of virtual currencies or other cryptocurrencies, counterfeiting of virtual currencies, cloning or restricting access to digital wallets, etc. are all taken into consideration.
Elnaz Rabieinejad, Abbas Yazdinejad, Reza M. Parizi
Blockchain technology has found extensive applications in recent years, especially in financial and currency exchange applications, due to improved trustworthiness and security. Although blockchain technology improves security by design, it is not immune to security threats and vulnerabilities. Ethereum, as a decentralized, open-source blockchain, has shown high growth and widespread adoption in recent years, however, there is a wide range of vulnerability, security risks, and also attacks around it. To tackle such issues, machine learning could be a viable solution for threat hunting in the Ethereum blockchain. Machine learning algorithms, by analyzing the behav-ioral patterns, can achieve an insight for threat hunting. In this paper, we proposed a deep learning-based model for Ethereum threat hunting. The model applies a deep neural network for attack detection and uses a combination of machine learning algorithms (unsupervised with supervised algorithms) for attack classification. The performance evolution of the proposed model in terms of accuracy presents 97.72 % in Ethereum attack detection and 99.4% in attack classification.
Cryptocurrencies are a subset of virtual currencies that have been devised for anonymous payments made entirely independent of governments and traditional financial institutions. The payment system of cryptocurrencies is expanding at a rapid pace and has reached Ethiopia. This article examines the extent to which cryptocurrencies are regulated under Ethiopia’s national payment system and anti-money laundering legal norms. The study has employed doctrinal research supported by in-depth interviews. During the last decade, Ethiopia has adopted several legal frameworks that govern different aspects of the payments landscape, most notably regarding payment services and electronic money. The country has anti-money laundering legal norms that are embodied in domestic laws and international and regional instruments that it has ratified. However, these legal norms have strategic deficiencies in regulating cryptocurrencies. Thus, the government of Ethiopia should consider enacting a comprehensive law that regulates the payment system of cryptocurrencies.
This chapter explains the motives that make cybercrime attractive and discusses the categories of cybercrime and aims to understand the cybercriminal. It also discusses the Internet of Things (IoT) and cybercrime. Financial cybercrime is the biggest threat to companies and organizations. Cybercriminals may seek access to valuable data to sell them on the Dark Web. Cybercrimes were committed long before the Internet existed; as soon as computing technology became widely available individuals began to think about how they could take advantage of these new opportunities and cybercrime was born. The increasing presence of worldwide high-speed Internet makes people more likely to experience cybercrime as victims. The IoT encompasses millions of computer-based devices that transmit data over the internet autonomously. The internet has exponentially increased the production, distribution, and possession of child pornography images and child sexual abuse material. Cybercriminals use decentralized exchanges to launder currency with peer-to-peer trading of cryptocurrencies without a third-party service to hold the customer&s;s funds.
Dinesh P Srivasthav, Lakshmi Padmaja Maddali, R. Vigneswaran
Cryptocurrencies have elicited tremendous interest in the recent past due to their ability to enable financial transactions without the need for a central authority. The most appealing aspect of cryptocurrencies that garnered significant attention is its potential to enable (pseudo) anonymous transactions on the blockchain ledger. Unsurprisingly, this has led to rapid adoption of cryptocurrencies for unlawful activities by malicious actors in several ways. Hence, to detect these unlawful activities, it is essential to analyze the blockchain ledgers to derive insights by investigating anonymous transactions and activities, which is where blockchain forensics comes in. In this paper, we present a taxonomy mapping the identified high-level forensics features with the supporting forensics tools that we surveyed. We provide a comparison of the surveyed tools using three practical parameters (number of cryptocurrencies supported, number of features provided and ease of accessing services) and give an overview of their theoretical effectiveness in general with some open challenges identified.
Hye-Yeong Shin, Meryam Essaid, Sejin Park, Hongtaek Ju
Bitcoin is the most representative UTXO-based blockchain platform, and many studies have been conducted related to it. However, account-based blockchains such as Ethereum are not yet profoundly analyzed. There is an urgent need to track all cryptocurrency transactions involved with illegal activities to deanonymize and identify malicious users. To link users' accounts to real identities in both networks, we first need to examine the differences between Ethereum and Bitcoin to propose an efficient deanonymizing method. Therefore, this paper compares and analyzes the wallet address clustering method of Bitcoin and Ethereum.
Blockchain technology has recently entered the life of a modern person. This technology is quite young. It is developing very rapidly. More and more industries are adopting this technology. However, technical regulation procedures lag behind the development of the technology itself. There are a number of issues related to the security of personal data. This article examines the current vulnerabilities of modern blockchain systems. An attempt was made to systematize vulnerabilities depending on their nature of occurrence. The reasons for the emergence of such vulnerabilities are identified, primarily related to the lack of international standards for the development of such systems.
As the mainstream of smart contract research, most Ethereum smart contracts do not open their source code, and the bytecode of smart contracts has attracted the attention of researchers. Based on the similarity measurement of smart contract bytecode, a series of tasks such as vulnerability mining, contract upgrading and malicious contract detection can be carried out. This paper proposes a method to measure the similarity of smart contract bytecode. Firstly, the key opcode combination of smart contract is summarized. When traversing the CFG(control flow graph) constructed by decompilation of smart contract bytecode, the opcodes in the basic block are pattern matched, and the features between the basic blocks are extracted according to the in-out degree, so as to enhance the similarity measurement effect of contract semantics in vector space. The experimental results show that the proposed method is greatly improved compared with the baseline.
Ali Raheem, Rand Raheem, Tom Chen, Ahmed Alkhayyat
Ransomware is one of the malicious software that is designed to prevent access to computer system until a sum of money is paid by the victim to the attacker. During the infection, the computer will either be locked, or the data will be encrypted. Ransoms are often demanded in Bitcoin, a largely anonymous Cryptocurrency. All transactions are recorded in the blockchain and verified by peer-to-peer networks. This paper investigation collects ten recent ransomware families, which use bitcoin as a payment for their ransom. In conjunction, we identified, collected and analysed Bitcoin addresses of users combining information from a clustering model and the blockchain. We used a heuristic clustering algorithm to reveal the hidden node’s payment of ransomware. Finally, we demonstrated the characteristics of ransomware encryption mechanisms that include a view of the infected process and its execution, and the distinctive demands of ransom.
The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least \$16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.
Simona Ramos, Fabio Pianese, Thomas Leach, Ester Oliveras
A non-traditional type of financial asset, cryptocurrencies based on public blockchains are still little understood in their real-world behavior. Exogenous events such as cyber-attacks and their media coverage can strongly affect their supply and demand, adoption and usage, efficiency, and infrastructural development, thus influencing their price stability and market valuation. Given the great technical complexity of blockchains, we believe that a pure economic analysis of risks associated with cryptocurrencies is simply not sufficient to convey the relationships between attacks and the disruptive effects that these can bring on the operation of cryptocurrencies. On these grounds, we survey the most common types of attacks for Proof-of-Work (PoW) cryptocurrencies and evaluate their impact on the returns of a number of real-world cryptocurrencies for which market data are available. Due to data availability, our event study analysis focuses on instances of 51% attacks, hard forks, and wallet attacks. The main goal of our analysis is to understand the relationship between technical events (cyber-attacks and coordinated user/miner behavior) and the economic impacts surrounding them. We aim to develop a deeper understanding of these systems that are objects of great research interest in separate disciplines, supporting policymakers in their regulatory decisions concerning crypto-assets and associated cyber-related financial risks.
The prevalence of Bitcoin has attracted a mass of investors into the blockchain ecosystem. Unfortunately, benefiting from its anonymity and immutability, scammers deploy various traps in smart contracts to exploit other participants and seize illegal proceeds. To identify smart Ponzi contracts-a classic fraud widely popular on Ethereum, previous studies present several machine learning-based models with considerable accuracy. However, the performance of their models relies on the behavioral features of smart contracts to a large margin, which are extracted from the transaction records only after a contract has been running for some time. In this paper, we borrow ideas from text feature extraction from Natural Language Processing (NLP) to build a classification model based on an improved CatBoost algorithm. A novel feature extraction pattern is applied in our model to deeply mine the logic of smart contract code. This approach can be used to detect Ponzi schemes at deployment time with improved performance, and thus can avoid the loss of investors originally.
Ahmet Faruk Aysan, Hüseyin Bedir Demirtaş, Mustafa Saraç
Blockchain is a path-breaking paradigm, and cryptocurrencies are one of the main application areas of Blockchain technology. Bitcoin leads the cryptocurrency markets, both in terms of market capitalization and in scientific interest. In this paper, we performed a comprehensive bibliometric study of the Bitcoin-related literature. Using the Scopus database, we created a sample that comprises 4495 documents written in the 2011–2020 period. Furthermore, we provided insights about dimensions such as the change in the number of publications over the course of years, the main research areas, types of published documents, most important platforms and sources of Bitcoin publications, highly cited studies, productive authors, author’s countries, and finally main funders of Bitcoin-related research. Lastly, our bibliometric study manifests the current state and future path of Bitcoin literature from distinct perspectives.
This paper explores how cryptocurrency affects Nigeria’s socio-economic and digital landscape, highlighting both its potential as an instrument of economic leveraging and its use in cybercrime. It explores how the decentralized characteristics of cryptocurrencies facilitate innovation and financial inclusion — and allow for anonymity, too, that enables cybercriminal activities. Today, cryptocurrency is a hot topic around the world. Nigeria, which registers some of the highest cryptocurrency adoption rates in the world, has used this class of digital asset as a key enabler of both breaking with traditional banking constraints and promoting economic resilience. But its decentralized and anonymous design also stands as a superpower to various forms of cybercrimes, from fraud and ransomware to money laundering. Such duality offers serious challenges to policymakers, law enforcement agencies, and financial institutions. The paper explores Nigeria’s regulatory experiences, technological barriers, and ethical dilemmas through in-depth analysis and case studies. This paper analyzes the phenomenon of cryptocurrency and cybercriminality in Nigeria, a phenomenon in which innovative technologies can be used as a vehicle for socioeconomic empowerment but also as a space for illegal activities. It examines case studies and statistical analyses revealing the systemic exploitation of cryptocurrency by cybercriminals and assessing the effectiveness of current legal and regulatory frameworks. This is against international best practices that expose Nigeria to grave dangers in tackling cybercrime linked to cryptocurrencies. We make sure that both the opportunities and threats it offers become bearable in how we balance the opportunities with threats this technology presents. Prescribing solutions for these challenges, the report highlights the importance of improving the regulatory environment, enhancing law enforcement capability in cyberspace, and developing a partnership between the private and public sectors to strike an appropriate balance between innovation and security. The research sent a clear message that articulated an approach to policy that would make the most of the potential of cryptocurrency while mitigating its weaknesses. Addressing these matters will allow Nigeria to seize the opportunities presented by cryptocurrency without compromising its frontier of digital security.
With rapid urbanization and the advancement of cities and towns, the graph of crime rates is increasing gradually. Blockchain can replace those piled up criminal records with a network where documents are easily accessible and could not be tampered with, making them safe and Secure. Blockchain is a P2P (peer-to-peer network) that helps in the decentralization of data. This system will be based upon the immutability characteristic of blockchain to ensure the integrity and security of data. This blockchain-based process can reduce corruption risk factors by making it easier for third parties to monitor tamper-evident transactions and enabling greater objectivity and consistency, thus enhancing criminal record transparency and accountability. Furthermore, timely access of authentic criminal records to respective administrative authorities will make law enforcement effective.
Purpose This paper aims to examine the framework for the regulation of crypto assets in Germany, the UK and Switzerland focusing on anti-money laundering (AML) laws. It comprehensively addresses the risks of crypto assets and the benefits along with the changes made to the existing laws to regulate cryptocurrency. Design/methodology/approach Qualitative data was analyzed to collect information for the case study and to challenge/examine the existing data and statistics. Findings The findings suggested that the AML laws are additionally modified to include the cryptocurrencies violations of the legislation, as it is the decentralized financial systems generating opportunities for crimes and terror financing. The moderate or mild laws were found in Switzerland following Germany and the UK has the most traditional and stringent laws of money laundering. Originality/value The paper has focused on the comparison of the three states in their AML laws comprehensively along with their attitude toward the crypto businesses.
Abdul Razaque, Abrar Al Ajlan, Noussaiba Melaoune, Munif Alotaibi · 9 authors
Modern information technology (IT) is well developed, and almost everyone uses the features of IT and services within the Internet. However, people are being affected due to cybersecurity threats. People can adhere to the recommended cybersecurity guidelines, rules, adopted standards, and cybercrime preventive measures to largely mitigate these threats. The ignorance of or lack of cybersecurity knowledge also causes a critical problem regarding confidentiality and privacy. It is not possible to fully avoid cybercrimes that often lead to sufficient business losses and spread forbidden themes (disgust, extremism, child porn, etc.). Therefore, to reduce the risk of cybercrimes, a web-based Blockchain-enabled cybersecurity awareness program (WBCA) process is introduced in this paper. The proposed WBCA trains users to improve their security skills. The proposed program helps with understanding the common behaviors of cybercriminals and improves user knowledge of cybersecurity hygiene, best cybersecurity practices, modern cybersecurity vulnerabilities, and trends. Furthermore, the proposed WBCA uses Blockchain technology to protect the program from potential threats. The proposed program is validated and tested using real-world cybersecurity topics with real users and cybersecurity experts. We anticipate that the proposed program can be extended to other domains, such as national or corporate courses, to increase the cybersecurity awareness level of users. A CentOS-based virtual private server is deployed for testing the proposed WBCA to determine its effectiveness. Finally, WBCA is also compared with other state-of-the-art web-based programs designed for cybersecurity awareness.
The decentralization, redundancy, and pseudo-anonymity features have made permission-less public blockchain platforms attractive for adoption as technology platforms for cryptocurrencies. However, such adoption has enabled cybercriminals to exploit vulnerabilities in blockchain platforms and target the users through social engineering to carry out malicious activities. Most of the state-of-the-art techniques for detecting malicious actors depend on the transactional behavior of individual wallet addresses but do not analyze the money trails. We propose a heuristics-based approach that adds new features associated with money trails to analyze and find suspicious activities in cryptocurrency blockchains. Here, we focus only on the cyclic behavior and identify hidden patterns present in the temporal transactions graphs in a blockchain. We demonstrate our methods on the transaction data of the Ethereum blockchain. We find that malicious activities (such as Gambling, Phishing, and Money Laundering) have different cyclic patterns in Ethereum. We also identify two suspicious temporal cyclic path-based transfers in Ethereum. Our techniques may apply to other cryptocurrency blockchains with appropriate modifications adapted to the nature of the crypto-currency under investigation.
Siddhartha R. Dalal, Zihe Wang, Siddhanth Sabharwal
Due to the pseudo-anonymity of the Bitcoin network, users can hide behind their bitcoin addresses that can be generated in unlimited quantity, on the fly, without any formal links between them. Thus, it is being used for payment transfer by the actors involved in ransomware and other illegal activities. The other activity we consider is related to gambling since gambling is often used for transferring illegal funds. The question addressed here is that given temporally limited graphs of Bitcoin transactions, to what extent can one identify common patterns associated with these fraudulent activities and apply them to find other ransomware actors. The problem is rather complex, given that thousands of addresses can belong to the same actor without any obvious links between them and any common pattern of behavior. The main contribution of this paper is to introduce and apply new algorithms for local clustering and supervised graph machine learning for identifying malicious actors. We show that very local subgraphs of the known such actors are sufficient to differentiate between ransomware, random and gambling actors with 85% prediction accuracy on the test data set.