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Example of a pump-and-dump chat group with over 40,000 members. Left: Telegram group ‘Rocket dump’. Right: Corresponding exchange data (Binance) of the targeted coin (Yoyo) showing the effect of the pump. The yellow, purple, and maroon lines represent the moving average for the last 7, 25, and 99 days respectively
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Abstract Pump-and-dump schemes are fraudulent price manipulations through the spread of misinformation and have been around in economic settings since at least the 1700s. With new technologies around cryptocurrency trading, the problem has intensified to a shorter time scale and broader scope. The scientific literature on cryptocurrency pump-and-du...
Citations
... HD Pump integrates techniques from anomaly detection [Olteanu et al., 2023] and change point detection [Truong et al., 2020]. Besides that, it demonstrates a significant performance improvement, achieving an 84.3% recall, 56.8% precision, and 67.9% F1-score compared to the strict configuration of Kamps and Kleinberg [2018], which scored 75.0% recall, 50.1% precision, and 60.5% F1-score on the same datasets. ...
... The pump-and-dump scheme has a long history in the stock market and a straightforward premise [Kamps and Kleinberg, 2018]. Initially, the perpetrators identify a publicly traded security, typically of small size and with low trading volumes, as their target. ...
... The perpetrators then release a biased review, highlighting supposed high profitability for participants [Xu and Livshits, 2019]. Kamps and Kleinberg [2018] was one of the pioneering studies in cryptocurrency PD schemes. It offered an early formalization of these schemes and presented a methodology for detecting them using AD algorithms. ...
The adoption of cryptocurrencies has created a favorable environment for price manipulation practices, such as pump-and-dump (PD) schemes. These schemes aim to artificially inflate an asset's price, followed by a rapid sell-off, which may harm unaware investors. Given the brief duration of PD scheme effects, their impact on the asset's price series can be considered anomalies. Most studies rely on classification-based anomaly detection techniques to identify the PD event, which presents an opportunity to explore techniques beyond anomaly detection. To address this, we explore the combination of anomaly and change point detection to enhance pump-and-dump scheme detection. We introduce HD Pump, a hybrid detection method that integrates both techniques. Experimental results demonstrate that our hybrid approach significantly improves performance, achieving a 6.7% increase in precision and a 9.3% increase in recall compared to the benchmark method that solely uses anomaly detection.
... With substantial funds flowing into DeFi applications, the allure of scams and cyber attacks has grown. Cryptoeconomic strategies, such as transaction reordering [100,118], flash loan abuse [164], arbitrage opportunities [44], and pump-and-dump schemes [221,98], have been adapted to exploit vulnerabilities in the NFT market. ...
The Non-Fungible Tokens (NFTs) has the transformative impact on the visual arts industry by examining the nexus between empowering art practices and leveraging blockchain technology. First, we establish the context for this study by introducing some basic but critical technological aspects and affordances of the blockchain domain. Second, we revisit the creative practices involved in producing traditional artwork, covering various types, production processes, trading, and monetization methods. Third, we introduce and define the key fundamentals of the blockchain ecosystem, including its structure, consensus algorithms, smart contracts, and digital wallets. Fourth, we narrow the focus to NFTs, detailing their history, mechanics, lifecycle, and standards, as well as their application in the art world. In particular, we outline the key processes for minting and trading NFTs in various marketplaces and discuss the relevant market dynamics and pricing. We also consider major security concerns, such as wash trading, to underscore some of the central cybersecurity issues facing this domain. Finally, we conclude by considering future research directions, emphasizing improvements in user experience, security, and privacy. Through this innovative research overview, which includes input from creative industry and cybersecurity sdomain expertise, we offer some new insights into how NFTs can empower visual artists and reshape the wider copyright industries.
... The memecoin market, with its high risks and rapid price swings, resembles a gambling arena where the thrill is tied to economic outcomes rather than the experience of social or humorous engagement. These characteristics -subject to herd mentality and vulnerable to manipulative schemes (Cary, 2021;Kamps & Kleinberg, 2018) -emphasise that while social support is instrumental in shaping investment decisions, it is the lure of monetary success that delivers the true adrenaline rush to investors. Bouteska et al. (2023) argue that memecoins, such as Dogecoin, are more comparable to social media phenomena and endorsements from public figures than more established cryptocurrencies, which have gained a firmer market foothold and thus exhibit lesser volatility. ...
Situated at the intersection of traditional finance and the rapidly evolving meme economy, memecoins present a compelling case for analysing investment motivations. Employing structural equation modeling, this research analyzes data from n = 153 participants experienced in cryptocurrency or stock market investments. Insights from motivational and social support theories help elucidate the relationship between perceived economic value and intrinsic investment factors. Results highlight that perceived economic value enhances the enjoyment of investing. Furthermore, social support influences perceptions of memecoins’ economic value, while perceived ease of investment affects only investment enjoyment, without significant impacts on economic value or investment intentions. This study enriches understanding in the nascent memes and finance field, offering empirical insights to guide future research and policy.
... Stolen data refers to the electronic information collected as a result of "exploitation of the vulnerability in a computer system or an unauthorized leak by someone with access to the data" (Thomas et al. 2017, p. 1). Several of such stolen data markets have been discovered in the application called 'Telegram' (Kamps and Kleinberg 2018). ...
Illicit data markets have emerged on Telegram, a popular online instant messaging application, bringing together thousands of users worldwide in an unregulated exchange of sensitive data. These markets operate through vendors who offer enormous quantities of such data, from personally identifiable information to financial data, while potential customers bid for these valuable assets. This study describes how Telegram data markets operate and discusses what interventions could be used to disrupt them. Using crime script analysis, we observed 16 Telegram meeting places encompassing public and private channels and groups. We obtained information about how the different meeting places function, what are their inside rules, and what tactics are employed by users to advertise and trade data. Based on the crime script, we suggest four feasible situational crime prevention measures to help disrupt these markets. These include taking down the marketplaces, reporting them, spamming and flooding techniques, and using warning banners.
... In addition to assessing the anonymity inherent in cryptocurrencies, the academic literature has increasingly focused on a range of financial crimes facilitated by cryptocurrencies (Braaten and Vaughn, 2019;Dawes Centre for Future Financial Crime, 2021;Durrant and Natarajan, 2019;Gandal et al., 2018;Grobys, 2021;Kamps and Kleinberg, 2018;Teichmann and Falker, 2021;Trozze et al., 2021;Trozze et al., 2022). Some examples of offenses that have been committed using cryptocurrencies include hacks, initial coin offering (ICO) scams, Ponzi schemes, market manipulation, money laundering, sanctions violation, tax evasion, theft, malware and other fraud (Gandal et al., 2018;Kamps and Kleinberg, 2018;Reynolds and Irwin, 2017;Teichmann and Falker, 2021;Trozze et al., 2022). ...
... In addition to assessing the anonymity inherent in cryptocurrencies, the academic literature has increasingly focused on a range of financial crimes facilitated by cryptocurrencies (Braaten and Vaughn, 2019;Dawes Centre for Future Financial Crime, 2021;Durrant and Natarajan, 2019;Gandal et al., 2018;Grobys, 2021;Kamps and Kleinberg, 2018;Teichmann and Falker, 2021;Trozze et al., 2021;Trozze et al., 2022). Some examples of offenses that have been committed using cryptocurrencies include hacks, initial coin offering (ICO) scams, Ponzi schemes, market manipulation, money laundering, sanctions violation, tax evasion, theft, malware and other fraud (Gandal et al., 2018;Kamps and Kleinberg, 2018;Reynolds and Irwin, 2017;Teichmann and Falker, 2021;Trozze et al., 2022). Teichmann and Falker (2021) conducted a detailed analysis of the methodologies used by money launderers to launder funds using cryptocurrencies. ...
... Echoing this focus, Trozze et al. (2022), while deliberately excluding a discussion of problems related to money laundering or areas of crime other than fraud, used a combination of a literature survey and an expert consensus exercise to ascertain that Ponzi schemes/high-yield investment programs, and ICO scams were the most frequently discussed cryptocurrency frauds across all literature. To combat financial crimes via cryptocurrencies, the scholarly literature has also directed attention toward the identification and detection of such illicit behavior (Farrugia et al., 2020;Gandal et al., 2018;Grobys, 2021;Kamps and Kleinberg, 2018;Park and Youm, 2021;Rognone et al., 2020;Shi et al., 2019;Sureshbhai et al., 2020). Such a focus underscores the importance of and progress in developing advanced techniques for the surveillance and prevention of cryptocurrencyrelated financial crimes. ...
Purpose
This paper aims to investigate technological innovations within the crypto space that have engendered novel financial crime risks and their potential utilization amidst geopolitical conflicts.
Design/methodology/approach
The theoretical paper uses an analysis of recent geopolitical events, with a key focus on using cryptocurrencies to undertake illicit activities.
Findings
The study found that cryptocurrencies and the innovations made within the crypto domain are used for both legitimate and illicit purposes, including money laundering, terrorism financing and sanction evasion.
Originality/value
This research contributes to understanding the critical role cryptocurrencies play amidst geopolitical conflicts and emphasizes the need for regulatory considerations to prevent their misuse. To the best of the authors’ knowledge, this paper is the first scholarly contribution that considers the evolving mechanisms afforded by cryptocurrencies amidst geopolitical conflicts in undertaking illicit activities.
... In this step, we fit and train a supervised learning model based on the labels derived in the previous step. According to the relevant research results in the model selection [35] [42] [43], SVM and RF algorithms show strong robustness and applicability in financial time series. Due to this, they are chosen to solve this problem. ...
... However, as there are few intermediate points under the threshold of 6%, abnormal points are concentrated on the edge of the distribution yet dense under the threshold of 18%. In contrast, the threshold of 12% makes the distribution of abnormal points and normal points more reasonable, which is in line with the experience of futures trading and the conclusion of relevant literature [42] [43]. Therefore, we decided to use the detection results under the threshold 12% for further research. ...
The price of crude oil is one of the most critical factors of the world economy, as it is volatile and sensibly affected by the macro-economic, thus attracting large-scale speculative activities. Well known for its vulnerability, some price fluctuations are affected by external factors, such as the financial crisis and oil trade war, while others are due to manipulation. Due to such facts, this article aims to clarify the characteristics and features of the market corner in the crude oil future market and detect potential market corner risk in West Texas Intermediate (WTI), and a hybrid anomaly detection approach is proposed. After a detailed overview of the characteristics and definitions of the market corner, the features are extracted by processing actual market data. We first detect suspicious market corners with abnormal prices and volume through the Local Outlier Factor algorithm (LOF). Next, the detected results are used as pseudo-labels, and the entire month's trading behavior is trained and classified through the Support Vector Machine (SVM) and Random Forest (RF) algorithms to identify potential market corners.Experimental results show that the proposed model has excellent accuracy, precision, recall, and F-score, indicating that the model is feasible and has strong robustness. Furthermore, based on the successful detection of potential market corner risk, the model can be further used for individual risk control and overall supervision of the crude oil futures market.
... Two studies have also examined how traditional financial market frauds, such as Ponzi schemes and pump-and-dumps have modernized with cryptoassets: they can now be pre-programmed in smart contracts [11,32]. Additional studies have also investigated the various forms of scams taking place in the DeFi ecosystem [31,52,71]. For example, the prevalence of phishing scams was investigated by [71] which identified 300 fake exchanges apps and 1,595 scam domain. ...
... The uncovered fake applications affected a total of 38 legitimate exchanges, which included almost all major cryptoasset exchanges. Another study explained that some phishing exchanges were solely created to get users to voluntarily deposit assets, while others were created to replicate a wallet extension and steal users' private keys [31]. Other studies also investigated imitation scams or giveaway scams [31,52], where an attacker typically imitates a celebrity on social media, claiming to giveaway cryptoassets. ...
... Another study explained that some phishing exchanges were solely created to get users to voluntarily deposit assets, while others were created to replicate a wallet extension and steal users' private keys [31]. Other studies also investigated imitation scams or giveaway scams [31,52], where an attacker typically imitates a celebrity on social media, claiming to giveaway cryptoassets. A study found that the average transaction value sent by users to different forms of giveaway scams ranged from USD 300 to USD 1,312 [52]. ...
Decentralized finance (DeFi) has been the target of numerous profit-driven crimes, but the prevalence and cumulative impact of these crimes have not yet been assessed. This study provides a comprehensive assessment of profit-driven crimes targeting the DeFi sector. We collected data on 1153 crime events from 2017 to 2022. Of these, 1,048 were related to DeFi (the main focus of this study) and 105 to centralized finance (CeFi). The findings show that the entire cryptoasset industry has suffered a minimum loss of US$30B, with two thirds related to CeFi and one third to DeFi. Focusing on DeFi, a taxonomy was developed to clarify the similarities and differences among these crimes. All events were mapped onto the DeFi stack to assess the impacted technical layers, and the financial damages were quantified to gauge their scale. The results highlight that during an attack, a DeFi actor (an entity developing a DeFi technology) can serve as a direct target (due to technical vulnerabilities or exploitation of human risks), as a perpetrator (through malicious uses of contracts or market manipulations), or as an intermediary (by being imitated through, for example, phishing scams). The findings also show that DeFi actors are the first victims of crimes targeting the DeFi industry: 52.2% of events targeted them, primarily due to technical vulnerabilities at the protocol layer, and these events accounted for 83% of all financial damages. Alternatively, in 40.7% of events, DeFi actors were themselves malicious perpetrators, predominantly misusing contracts at the cryptoasset layer (e.g., rug pull scams). However, these events accounted for only 17% of all financial damages. The study offers a preliminary assessment of the size and scope of crime events within the DeFi sector and highlights the vulnerable position of DeFi actors in the ecosystem.
... Em outro estudo, Jiahua Xu e Livshits [Xu and Livshits 2019] apresentaram uma análise detalhada de esquemas "Pump and Dump"em criptomoedas e demonstraram qué e possível prever com cerca de 90% de precisão quais criptomoedas são visadas por esse tipo de fraude. Kamps e Kleinberg [Kamps and Kleinberg 2018] também utilizaram dados públicos de transação para detectar fraudes de manipulação de preços "Pump and Dump"em criptomoedas. Jason Brownlee desenvolveu um método de classificação de séries temporais usando Redes Neurais Artificiais (RNA) [Brownlee 2018], que se mostraram mais eficazes do que outros algoritmos de aprendizado de máquina, como Naive Bayes, KNN e Random Forest, na classificação de séries temporais. ...
Este artigo apresenta um método baseado em modelos preditivos de redes neurais para detecção de fraudes em criptomoedas provenientes de Initial Coin Offering (ICO). Através da análise de Séries Temporais geradas a partir de tabelas de fluxo de transações na rede Ethereum, foram desenvolvidas 5 séries temporais normalizadas que serviram como entrada para os modelos de Redes Neurais Artificiais (RNA) MLP, CNN-MLP e LSTM-MLP projetados para classificação. Dado que 78% das atividades de ICO são fraudulentas, este método é um importante passo em direção à prevenção de fraudes em criptomoedas. Os resultados obtidos na pesquisa foram bastante satisfatórios, com um valor de Recall de até 91% em alguns casos.
... While high-cap projects may be vulnerable to money laundering and phishing scams, low-cap projects may be targeted for pump and dump schemes. An overview is provided in Table 5. [4,45,82,113,114,121,127,129,143,145,158,162,173,179] Phishing scam Websites (including giveaway scams) Cryptocurrencies, NFTs [107,111,138,157] Directly transfer to phishing accounts Mainly Ethereum [29, 63, 66, 81, 92, 109, 124, 142, 167, 169, 180, 185, 188-191, 193, 194, 201, 203] Pump and dump Rapid price change, closely related to social media activities Mainly coins [32,80,91,100,101,125,164,186] 7.1 Money Laundering ...
... Pump and dump (P&D) schemes artificially inflate asset prices through coordinated buying and spreading misleading information [91]. In DeFi, researchers have studied P&D schemes since 2018, examining influencing factors, impact, and underlying mechanisms [48,70,71,110]. ...
... (2) Detecting P&D events -Scholars have also employed sophisticated statistical and machine learning techniques to detect and prevent P&D activities in DeFi. Kamps and Kleinberg [91] introduced a framework for P&D detection with a thresholding algorithm to identify suspicious points of anomalous trading activity in exchange. La Morgia et al. [100] extended this work by applying machine learning models, including RF and LR, to detect P&D events using ground truth extracted from social media platforms. ...
In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.
... They then sell their holdings to make a profit, causing the price to fall and investors to lose money. Kamps and Kleinberg (2018) and La Morgia et al. (2020Morgia et al. ( , 2023 examined large sets of pumps-and-dumps and proposed several market-based approaches to detect them. Unfortunately, the detection of pump-and-dumps with crypto assets and high-frequency data involves imbalanced datasets where the minority class flagging the pump-and-dumps is very small (usually less than 0.2% of all cases). ...
... Nghiem et al. (2021) proposed a method using market and social media signals to predict target cryptocurrencies for pump-and-dump schemes, achieving superior results compared to existing methods. Kamps and Kleinberg (2018) developed a machine learning model trained on pump-and-dump event data to effectively detect these schemes. Based on this analysis, La Morgia et al. (2020) conducted an in-depth examination of community-organized pumps and dumps, reporting several case studies and providing a real-time detection classifier for investors that outperformed all the other competing models. ...
... This is because such schemes are often associated with imbalanced datasets, where the instances of pump-and-dump cases are very rare, usually comprising less than 0.2% of all cases. Additionally, it is widely assumed that members of social media groups that organize these market manipulations are given detailed information about the scheme seconds or minutes before it is made public; see Kamps and Kleinberg (2018) and La Morgia et al. (2020Morgia et al. ( , 2023. They pay a fee for such advanced knowledge, but in reality, unusual trading volumes can occur much earlier than that, complicating the process of detecting such schemes. ...
Detecting pump-and-dump schemes involving cryptoassets with high-frequency data is challenging due to imbalanced datasets and the early occurrence of unusual trading volumes. To address these issues, we propose constructing synthetic balanced datasets using resampling methods and flagging a pump-and-dump from the moment of public announcement up to 60 min beforehand. We validated our proposals using data from Pumpolymp and the CryptoCurrency eXchange Trading Library to identify 351 pump signals relative to the Binance crypto exchange in 2021 and 2022. We found that the most effective approach was using the original imbalanced dataset with pump-and-dumps flagged 60 min in advance, together with a random forest model with data segmented into 30-s chunks and regressors computed with a moving window of 1 h. Our analysis revealed that a better balance between sensitivity and specificity could be achieved by simply selecting an appropriate probability threshold, such as setting the threshold close to the observed prevalence in the original dataset. Resampling methods were useful in some cases, but threshold-independent measures were not affected. Moreover, detecting pump-and-dumps in real-time involves high-dimensional data, and the use of resampling methods to build synthetic datasets can be time-consuming, making them less practical.
