# A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning
## 2 Background and Related Work
### Related Works  
In this section, the related surveys and systematic literature reviews regarding market manipulation detection in stock markets and cryptocurrencies are mentioned. [PERSON] and [PERSON] (2019) conducted a thorough evaluation of the literature on market  
Figure 4: BAR/USDT P&D event organized in a telegram on 20 June 2023. The increase percentage of green candle which indicates pump phase is shown by blue line and the following red candle is dump phase of event. A one-minute time frame was chosen to visualize the candles. Figure 3: Screenshots of Telegram P&D Group. (**a**) Preparation Announcement; (**b**) Countdown; (**c**) Countdown; (**d**) Coin Announcement (the target coin is shown by green outline). manipulation detection from 2010 to 2020. The authors examined 52 key papers and evaluated definitions, taxonomies, objectives, techniques, strengths and weaknesses, proposed solutions, data mining approaches, and types of manipulations and anomalies. They examined several types of market manipulation including P&D. The study provided several significant outcomes, including realistic solutions for manipulation detection, mapping manipulations with anomalies, identifying obstructions and open questions in the field, and identifying taxonomy criteria for trade-based manipulation. They contributed greatly to showing how electronic financial markets are manipulated and how to detect them. However, they only focused on the manipulations in the stock market, while the crypto market as an electronic financial market was neglected in their study. Furthermore, they indicated an imbalance in focus across various manipulations, with certain manipulations receiving significantly more attention in the studies compared to others. Additionally, they stated that more substantial and in-depth research is needed to develop practical and accurate methods for detecting manipulation. In the survey, it was mentioned that the study's shortcomings include a limited time span (2010-2020), which imply the need for future extended assessments. Manipulation cases are increasingly complex, making it impossible to observe them manually. To detect them, intelligent systems have been developed. [PERSON] et al. (2011) conducted a review that focused on methods and algorithm approaches in conventional machine learning (ML) and deep learning (DL). In their report, they stressed the importance of well-managed financial markets and the role that regulators play in preventing illegal trading. They also emphasized on the effectiveness of using artificial intelligence in detecting manipulations, particularly the preference for tick data over daily trading data when detecting manipulations. It turned out that support vector machine (SVM) classifiers were the most popular, and the detection rates were often higher with simpler classes. Furthermore, the paper outlined challenges facing researchers, such as regulatory differences, a lack of publicly available datasets and manipulation case listings, and the need for algorithm performance improvements. To enhance detection capabilities while addressing regulatory and data challenges, the authors suggested implementing DL analysis networks as well as incorporating advanced attention mechanisms and stock data from multiple countries. Nevertheless, this study also focused on detecting manipulations in stock markets and neglected the crypto market. Regarding the cryptocurrency markets, [PERSON] et al. (2013) conducted a systematic literature review to provide a comprehensive overview of cryptocurrency market manipulation methods. Seven manipulation methods in cryptocurrency markets are listed in their study, while six of them were already known from traditional markets. However, they stated that there is a gap between academic research and market activities; there may still be current market manipulation techniques that have not yet received academic attention. Furthermore, they identified six factors that lead to successful market manipulation schemes.