We examine cryptocurrency fraud cases prosecuted by Nigeria's Economic and Financial Crimes Commission (EFCC). We considered the lens of the Space Transition Theory (STT) in exploring the dynamics of these digital crimes. Our data analysis reveals common types of fraud, including cryptocurrency investment schemes. The results show an exclusive male demographic (100%), with the majority under 30 years old and only a quarter possessing a degree, providing insights into the socio-demographic characteristics of cryptocurrency fraudsters. Additionally, while most fraudsters (55%) targeted victims in the United States, Bitcoin, leveraging blockchain technology, was the most commonly used method (46%) for cryptocurrency fraud. Our examination of the methods and mediums used for cryptocurrency fraud supports some aspects of STT, while others do not. We advocate for a multifaceted strategy that prioritises stringent regulation, implementation, and heightened scrutiny of digital currency ecosystems in Nigeria and beyond. This study contributes to the broader discourse on cybercrime prevention and enforcement by emphasising the novel methodological approach utilised.
Eduardo Augusto de Medeiros Silva, Ivan da Silva Sendin
Selfish Mining is an attack on the proof-of-work-based cryptocurrency consensus mechanism, enabling attackers to gain more than their fair share of rewards. Its existence indicates that the Nakamoto consensus is not incentive compatible and could jeopardize blockchain security. Recently, a method employing the Z-Score to detect selfish mining was proposed. This paper introduces a non-parametric statistical technique to identify traces of selfish miners on the blockchain without assuming any specific statistical distribution for the analyzed data. Additionally, the applicability of this type of analysis is discussed.
Hany F. Atlam, Ndifon Ekuri, Muhammad Ajmal Azad, Harjinder Singh Lallie
Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics.
Cryptocurrency has emerged as a viable alternative to conventional payment systems, offering its users notable advantages such as cost efficiency and rapid transaction processing. However, cryptocurrencies’ decentralized nature, anonymity, and susceptibility to cyber threats introduce the potential for their exploitation in illicit activities, notably money laundering and terrorism financing (ML/TF). This scholarly investigation seeks to investigate the roles played by international organizations actively combating such criminal activities. In this academic context, a meticulous consideration of the inherent risks associated with the aforementioned criminal endeavors is undertaken, aligning with the guidelines and recommendations set forth by the Financial Action Task Force (FATF). The study underscores a significant finding that cryptocurrency accounts can be opened anonymously, with no centralized registry to monitor cryptocurrency ownership. This characteristic poses a formidable challenge in confiscating funds linked to terrorist activities. Even in instances where cryptocurrency transactions may be traced, accessing this critical data necessitates the involvement of third-party entities, given that cryptocurrency transactions are recorded in decentralized ledgers spanning multiple jurisdictions. Furthermore, the study underscores the imperative of coordination and information exchange as indispensable elements in the ongoing battle against organized and transnational criminal activities, specifically ML/TF. Moreover, the study elucidates the incongruence between cryptocurrency and FATF regulations governing electronic fund transfers, as cryptocurrencies can make transactions through opaque and unregulated conduits, such as the deep web.
This paper explores a monetary experiment, the adoption of Bitcoin as legal tender in El Salvador in 2021, to analyse the impact of digital currencies on international capital flows. Using a difference-in-differences approach, we find that, instead of making transfers easier, El Salvador’s official cross-border financial activity has decreased after the monetary change. This finding may reflect an increase in uncertainty. However, it is also in line with findings that link digital assets to illegal activity as previously officially recorded financial transfers may have been replaced by unrecorded activities.
Under the dual impact of the COVID-19 pandemic and the Russian-Ukrainian conflict, the excessive stimulation of monetary policy continuously pushes up global inflation (INF). Therefore, this article explores whether Bitcoin can serve as a safe haven for INF. We apply the rolling-window Granger causality test to solve the issue of parameter instability in vector autoregression (VAR) systems and investigate the time-varying interaction between INF and Bitcoin price (BP). The negative influence of INF on BP means a high inflation shock causes BP to decline, indicating that Bitcoin cannot be a safe asset against INF. This is because investors have decreased their willingness to hold Bitcoin under the high INF expectations and cause BP to fall. This finding is not supported by the Intertemporal Capital Asset Pricing Model, emphasising that INF positively impacts BP. Conversely, BP has positive and negative impacts on INF. The positive effect highlights the effectiveness of Bitcoin in predicting INF fluctuations, but economic factors could undermine this effectiveness. In the context of economic stagnation and market turmoil, investors can adjust their portfolio investments based on Bitcoin. The government should utilise the trend of BP to regulate the dynamics of INF to reduce uncertainty in the financial system. First published online 30 August 2024
Algimantas Venčkauskas, Šarūnas Grigaliūnas, Linas Pocius, Rasa Brūzgienė · 5 authors
Layering through cryptocurrency transactions represents a sophisticated mechanism for laundering money within cybercrime circles. This process methodically merges illegal funds into the legitimate financial system. Blockchain technology plays a crucial role in this integration by facilitating the quick and automated dispersal of assets across various digital wallets and exchanges. Machine learning emerges as a powerful tool for analyzing and identifying illicit transactions within Blockchain networks; however, a significant challenge remains in the form of a gap in advanced pattern recognition algorithms. This paper introduces a novel machine learning-based approach called Value-driven-Transactional tracking Analytics for Crypto compliance (VTAC) for the detection of illegal crypto transactions via Blockchain. The approach combines machine learning algorithms with a pre-training process, normalization, model training, and a de-anonymization process to analyze and identify illicit transactions effectively. Experimental evaluations show VTAC’s capability to detect illegal transactions with a 97.5% accuracy using the XG Boost model, outperforming existing methods with an accuracy of up to 95.9%. Key performance metrics, including precision, recall, and F1-score, consistently exceeded 95%, highlighting VTAC’s enhanced precision and reliability. The proposed solution will serve as an advisory framework to help financial crime investigators enhance the detection and reporting of suspicious cryptocurrency transactions in cyberspace.
This study explores the application of deep learning and machine learning technologies in the field of Anti-Money Laundering (AML) for cryptocurrencies.With the rapid growth of cryptocurrency markets, the associated money laundering activities have increasingly become a focal point for governments and financial institutions worldwide.Traditional AML measures face challenges in the digital realm, particularly in identifying and preventing illicit transactions involving cryptocurrencies.To address this, the study designs various algorithms including Deep Neural Networks (DNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB) to enhance the detection capabilities of suspicious transactions within the Bitcoin Elliptic dataset.Cryptocurrencies involve using cryptographic security measures for financial transactions, yet their anonymity and transnational nature make them susceptible to money laundering activities.By evaluating the performance of different machine learning models on the Bitcoin Elliptic dataset, this research analyzes their effectiveness in identifying illicit transactions.The results indicate that the Random Forest model performs best, achieving an overall accuracy of 95%, effectively distinguishing between most illegal and legal transactions while mitigating overfitting risks.Through these technological approaches, the study aims to enhance AML monitoring capabilities in cryptocurrency markets, providing reliable decision support for financial institutions and regulatory bodies.Future research directions may include exploring more complex deep learning models or ensemble learning methods to further improve classification accuracy across diverse datasets and enable real-time monitoring of emerging money laundering patterns.The integration of these technologies holds promise for strengthening the global AML framework, addressing the increasingly complex challenges posed by digital finance and illicit financial activities (
Aim: As a new asset class, Bitcoin and other cryptocurren-cies can be interesting for investors in the context of return stabilization, especially in times of crisis. We aimed to anal-yse whether Bitcoin can serve as a safe haven for investors in times of crisis. Methods: The data covers the period from September 17, 2014, to April 29, 2021, with 382 observations. Yahoo! Finance served as the source for the Bitcoin prices and Investing.com for the values of the Standard & Poor’s 500 (S&P500) Index. We used the maximum likelihood method to estimate the dynamic conditional correlation model. Results: Due to the high volatility during the analysed peri-od, Bitcoin achieved a higher risk-adjusted return compared to the S&P500 Index. The DCC model showed a positive cor-relation between the returns of the S&P500 and Bitcoin during the analysed period. Conclusions: Our results suggest that Bitcoin may not serve as a safe haven for investors in times of crisis. However, its role in this context should be further evaluated by examin-ing its relationship with other traditional asset classes (gold, commodities) and other types of cryptocurrencies such as stablecoins.
In the field of blockchain technology, smart contracts play a core role, but programming oversights may cause serious security risks. This study provides a comprehensive review of the types of smart contract security vulnerabilities and the development of detection techniques. This article conducts a comprehensive review of the current various detection methods, including static and dynamic analysis, and proposes a combined dynamic and static detection method for integer overflow vulnerabilities at the solidity code level and timestamp vulnerabilities at the blockchain system layer. It also analyzes a The performance of a series of mainstream detection tools in terms of detection accuracy and efficiency is compared in depth, and their advantages and limitations are analyzed.
Decentralized Finance (DeFi), a pivotal component of the emerging Web3 landscape, is gaining popularity but remains vulnerable to market manipulations, such as wash trading. Wash trading is an illegal practice, where traders buy and sell assets to themselves within cryptocurrency exchanges to artificially inflate trading volumes and distort market perceptions. However, current research primarily focuses on traditional exchanges based on the Order-book mechanism (similar to stock markets), while ignoring the Automated Market Maker (AMM) exchanges, which dominate over 75% of the market and represent a significant innovation within the DeFi. This study utilizes entity recognition technology to detect wash trading on AMM exchanges within Ethereum-like systems, based on the understanding that colluding addresses (perceived as the same entity) must use ETH for transaction fees and exhibit direct or indirect ETH transfer links. We identify wash trading when addresses with transfer connections almost simultaneously buy and sell assets while their total asset holdings remain nearly constant. This comprehensive blockchain network analysis, compared to focusing solely on transactions within exchanges, unveils covert wash trading activities. Our detection method achieves a 95.9% recall and a 96.7% true negative rate in identifying pools affected by wash trading, demonstrating its superiority over existing methods. Furthermore, we apply our method to 98,945 pools from Uniswap V2 & V3 (the most popular AMM exchanges on Ethereum) and identify 1,070,626 abnormal transactions, totaling $27.51 billion in trading volume. Analysis of these transactions uncovers insights into wash traders’ behaviors, including the utilization of multiple addresses and the dual roles of certain addresses as wash traders and liquidity providers. These insights are crucial for developing more effective strategies to combat fraudulent activities in the DeFi ecosystem and enhance financial scrutiny.
Aug 19, 2024·2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics
With the continuous advancement of blockchain technology, smart contracts, as one of its core applications, have increasingly become a focal point for security concerns. To address this, this paper proposes a novel method that integrates keyword filter technology, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) architecture, aimed at enhancing the efficiency and accuracy of vulnerability detection in Solidity smart contracts. Initially, keyword filter technology is employed to select and preprocess code snippets, extracting features closely associated with security vulnerabilities. Subsequently, the BERT model conducts deep semantic analysis and feature extraction, after which the BiLSTM-CRF architecture further learns from the features and predicts vulnerability types. Extensive experiments conducted on a dataset comprising eight major types of vulnerabilities demonstrate that the method proposed in this study significantly outperforms existing vulnerability detection methods in key metrics such as accuracy, recall, and F1 scores. This research not only provides an effective technical solution for detecting security vulnerabilities in smart contracts but also holds significant theoretical and practical implications for promoting the safe and reliable development of blockchain technology.
Purpose The purpose of this paper is to analyze FTX cryptocurrency frauds. FTX is a former cryptocurrency exchange platform that went bankrupt because of fraud in 2022. Design/methodology/approach Using a qualitative method and a case study of FTX, the authors document the multiple fraud schemes perpetrated. The authors collected media and research articles that discussed the FTX case. The authors analyzed 18 articles. Findings Based on this case, the authors highlight the governance and ethics weaknesses in the FTX environment. The authors also discuss cryptocurrency risks and regulation of cryptocurrencies. The FTX affair has shaken up the international regulatory world, which has been seeking solutions to protect customers and investors and helping banks take positions since 2022. Originality/value This study contributes to the fraud literature by deeply examining cryptocurrency fraud risks. In addition, the findings could help financial institutions and guide them in the cryptocurrency world.
Due to its anonymity and decentralization, Bitcoin has long been a haven for various illegal activities. Cyber-criminals generally legalize illicit funds by Bitcoin mixing services. Therefore, it is critical to investigate the mixing services in cryptocurrency anti-money laundering. Existing studies treat different mixing services as a class of suspicious Bitcoin entities. Furthermore, they are limited by relying on expert experience or needing to deal with large-scale networks. So far, multi-class mixing service identification has not been explored yet. It is challenging since mixing services share a similar procedure, presenting no sharp distinctions. However, mixing service identification facilitates the healthy development of Bitcoin, supports financial forensics for cryptocurrency regulation and legislation, and provides technical means for fine-grained blockchain supervision. This paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification (BMSI-GC) model. First, BMSI-GC constructs 2-hop ego networks (2-egonets) of mixing services based on their historical transactions. Second, it applies graph2vec, a graph classification model mainly used to calculate the similarity between graphs, to automatically extract address features from the constructed 2-egonets. Finally, it trains a multilayer perceptron classifier to perform classification based on the extracted features. BMSI-GC is flexible without handling the full-size network and handcrafting address features. Moreover, the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for identification. Our experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification, achieving an average identification F1-score of 95.08%.
Mohammad Nur Bobby Putra Yusra, Arthur Josias Simon Runturambi, Bondan Widiawan
In Indonesia, Cryptocurrency, on the one hand, is not recognized as a legal tender, so it does not have a legal umbrella, and the risk of its use is borne by the user himself. On the other hand, cryptocurrencies are included in the list of commodities that can be used as the subject of Futures Contracts traded on the Futures Exchange because, from their use, it is expected to make a positive contribution to futures trading in Indonesia. These two contradictory things of cryptocurrency regulation are then faced with a phenomenon called cryptocurrency-based TPPU. This study uses a descriptive method combined with a qualitative approach. The data in this study is primary data sourced from the results of interviews and data from the Metro Jaya Police, and the secondary data used to support the research is literature in the form of books, research journals and online scientific journal data. The data that has been collected is analyzed by data reduction methods and triangulation techniques. The location of the research is the Metro Jaya Police and the University of Indonesia Library. From this study, it is known that in Indonesia, there are no special rules governing this cryptocurrency-based anti-corruption, and there has been no cooperation between law enforcement for its prevention.
Hossein Mehri Khonakdari, Hossein Sadeghi Saghdel, Abbas Assari Arani, Amir Hossein Mozayani
Since 2008, when Bitcoin was introduced as the first cryptocurrency, the world has witnessed a rapid growth of cryptocurrencies. Along with the rapid growth of cryptocurrencies, the need for study and research in this field increases. The effects of the expansion of cryptocurrencies on the economy is one of the important challenges in this field. In this paper, the expansion of cryptocurrencies in Iran's economy has been investigated using the SWOT method. The strengths, weaknesses, opportunities, and threats of cryptocurrencies were examined and presented in the form of a 2×2 table. Then, to quantify the results, the AHP method was used and the items specified in the SWOT method were prioritised by using experts' opinions. Finally, the appropriate strategies in this field for Iran's economy were explained.
The coming of cryptocurrencies has, amazingly, changed the face of the financial space. It has opened up opportunities and challenges in the field of money laundering. The deep impact of cryptocurrencies on the practice of money laundering becomes a detailed study. Since digital currencies are embedded with inherent characteristics, such as anonymity, decentralization, and the ease of performing cross-border transfers, criminals have now found new ways to conceal their illicit financial activities. The paper critically reviews how cryptocurrencies are used in money laundering schemes, evaluates the effectiveness of current legal provisions and anti-money laundering measures, and reviews case studies that exemplify real-world applications and challenges to regulatory bodies. Moreover, it offers recommendations on the use of new technologies, like blockchain analytics, toward better detection and prevention of money laundering through cryptocurrency. The paper thus provides a range of useful insights, associated with recommendations for the strengthening of the global regulatory framework in dealing with the increased threat of cryptocurrency money laundering, through a synthesis of the literature review, case analysis, and expert interviews. The paper contributes to this debate by providing insight into the challenges that regulatory authorities face and making recommendations to improve anti-money laundering efforts in the cryptocurrency space. This is done through an in-depth review of recent cases and legislation in this area. The findings were that, though cryptocurrencies pose a great challenge, innovative technology solutions coupled with international cooperation can play a vital role in mitigating the risks associated with cryptocurrency-based money laundering.
In recent years, Smart Contracts have gained in popularity, facilitating billions of US Dollars in daily transactions. However, the recent increase in smart contract vulnerabilities threatens to undermine trust in the technology. The study aims to detect and address potential vulnerabilities in smart contracts in blockchain technology through a comprehensive analysis of four principal modalities: Solidity source code, bytecode, opcode, and intermediate representations. This proactive identification of vulnerabilities can contribute to bolstering the security and dependability of blockchain-based systems. In this paper, we propose a novel multimodal Transformer architecture named QuadraCode AI, utilizing these four distinct modalities. Unlike traditional unimodal analysis, multimodal analysis can provide a more holistic understanding of both the semantic and syntactical contexts of smart contracts to identify underlying vulnerabilities. By employing advanced data fusion techniques such as cross-attention and concatenations across 12 different multimodal frameworks, our approach enhances the detection capabilities beyond traditional unimodal approach. Notably, the framework that integrates opcode with bytecode achieves an impressive average F score of 86%, demonstrating the effectiveness of our method.
Purpose This paper aims to unveil the darker side of cryptocurrencies by delving into its role as an obstacle to investments in Middle East and African (MEAs) countries, unravelling the challenges involved. Indeed, despite the rise of blockchain-related technologies, specifically cryptocurrencies, having undeniably unlocked new avenues for business and society, crypto for venture funding purposes may exhibit a “dark side” due to their use for unethical purposes, for example, money laundering or terrorism financing, largely diffused in certain areas of MEA countries. Design/methodology/approach Through an explorative research design, using a mix of techniques based on both qualitative and interpretive methods, we conducted in-depth interviews among 33 European managers of companies engaged in MEA markets or aspiring to invest in such foreign markets, to analyse their thoughts, perceptions and possible strategies concerning the management of the “dark side” of cryptocurrencies in MEAs. Findings Our investigation unearthed seven pivotal issues, which manifest as significant barriers related to the ambivalent use of crypto for funding projects, encompassing seven important consequential elements: (1) lack of knowledge about the technology’s potentialities; (2) perceptions of crypto technology’s ambivalence; (3) reputation and image consequences; (4) uncertainty about the destination of the invested funds; (5) decreased attractiveness of MEAs; (6) competition and market; and (7) lack of control and regulation. We grouped these into technology-related, business-related and legal- and policy-related barriers. Such findings underline the probable decrease in attractiveness of MEAs in terms of investments, together with the triggering factors and potential strategic solutions to mitigate such circumstances. Research limitations/implications Future studies could explore a broader sample of managers since we only considered the perception of European managers operating in companies that invest (or are intending to invest) in MEAs. Moreover, future research may extend the analysis to MEA-native companies or those engaging in reciprocal exchanges with Western countries. Practical implications Practically, our findings suggest several elements in which to intervene to mitigate managers’ negative perception of the unethical use of cryptocurrencies in MEAs and to support CEOs’ and CFOs’ strategies, together with requirements to ensure the unaltered attractiveness of investments in an otherwise thriving region of the world, without overlooking the protection and safeguarding of investments and the health of the market and competition. Furthermore, a call for future research in this domain, along with at least minimal regulatory mechanisms, clearly emerges. Social implications Our findings underline the social challenges associated with the perception and acceptance of cryptocurrencies in these contexts, influencing cultural and social dynamics. Moreover, the identification of these barriers could underscore the significance of awareness of and education on blockchain technology and cryptocurrencies within society, including implications for policymakers. Originality/value Despite prior investigations into the negative effects of cryptocurrencies as a form of venture funding, no studies to date have examined managers’ perceptions by focusing on possible barriers to investment in MEA countries due to the unethical usage of crypto. Importantly, this paper unravels the unexplored complexities of crypto’s impact on ethical investments in MEAs, showcasing an original perspective.
The main purpose of this study is to provide a comprehensive assessment of the importance of cryptocurrencies in terms of money laundering risk and to provide detailed information on money laundering techniques and structures. An analysis of how cryptocurrencies impact local and international money laundering is also being explored to clarify the facts. The article attempts to provide a better understanding of this emerging problem by providing information on the use of digital currency to change the money laundering landscape. To clarify the review, the review is divided into two main parts: The first part will focus on the theoretical framework of the money laundering process, the process complexity of cryptocurrencies and the ecosystems surrounding them. The second part will examine whether virtual currencies are suitable for money laundering, the various features that make virtual currencies ideal for such activities, and the creation of new emerging technologies will also be discussed. This document is designed to provide policymakers, regulators, and law enforcement with useful information and strategic solutions to address the challenges of cryptocurrency money laundering through many of these methods.
Modern blockchains support the execution of application-level code in the form of smart contracts, allowing developers to devise complex Distributed Applications (DApps). Smart contracts are typically written in high-level languages, such as Solidity, and after deployment on the blockchain, their code is executed in a distributed way in response to transactions or calls from other smart contracts. As a common piece of software, smart contracts are susceptible to vulnerabilities, posing security threats to DApps and their users.
This paper examines the actions taken by commercial banks to develop effective surveillance strategies for the identification of the origins of clients' digital assets within the cryptocurrency sector. Informed by the European Union Agency for Cybersecurity (ENISA) guidelines, this study integrates insights from compliance expert interviews, a case study of Barclays, and a thorough analysis of existing literature to enhance cybersecurity measures in response to increasing threats in digital transactions such as cryptojacking. The research emphasizes constructing a methodical approach for organizations to enhance their established defenses against cryptocurrency-related risks by integrating ENISA principles. The proposed strategy focuses on improving transaction traceability and monitoring capabilities of blockchain technologies while acknowledging technological challenges related to regulatory and safety compliance. The provided in-depth analysis of blockchain and cryptocurrency technologies present in this paper advocates for strict regulatory frameworks to navigate these complexities and enhance overall resilience towards this threat.
Cryptocurrencies, especially privacy coins, conceal the flow of money. Similarly, the dark web obscures the flow of internet traffic, increasing anonymity. In this paper, I provide evidence that secondary market trading activity in privacy coins is linked to dark web traffic, although their pricing remains mostly unaffected. This finding holds after considering various controls and comparing similar privacy and non-privacy coins. However, when disentangling dark web traffic by country of origin, I find that privacy coin prices correlate positively with traffic from China, while trading volume is mainly driven by users from Russia and Iran.
Fernando Richter Vidal, Naghmeh Ivaki, Nuno Laranjeiro
The number of applications supported by blockchain smart contracts has been greatly increasing in recent years, with smart contracts now being used across several domains, such as the music industry, finance, and retail, to name a few. Despite being used in business-critical contexts, the number of security vulnerabilities in smart contracts has also been increasing, with many of them being exploited and resulting in huge financial and reputation losses. This is despite the enormous effort that is being placed into the research and development of vulnerability detection tools and techniques, which have also greatly increased in number and type in the last few years. Motivated by the recent increase in both vulnerabilities and vulnerability detection techniques, this paper reviews the latest research in smart contract vulnerability detection, emphasizing the techniques being used, the vulnerabilities targeted, and the characteristics of the dataset used for evaluating the technique. We mapped the vulnerabilities against two common vulnerability classification schemes (DASP and SWC) and performed a consolidated analysis. We identified the current research trends and gaps in each technique and highlighted future research opportunities in the field. • A categorization of smart contract vulnerability detection techniques. • The identification of smart contract vulnerabilities that are the target of current vulnerability detection tools. • An analysis of the datasets used in smart contract vulnerability research.