# A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning
## 5 Discussion and Research Directions
### Discussion  
The purpose of this section is to present an overview of the 13 studies that investigated the use of ML techniques in order to predict P&D manipulations in cryptocurrencies. Table 4 presents the strengths and weaknesses of these studies. Classical ML models were initially adopted by researchers because of their ease of implementation and the ability to achieve satisfactory performance on smaller datasets without overfitting. Subsequently, various studies explored the application of unsupervised ML approaches [13; 43; 45], while others employed supervised ML techniques, such as RF or LR [15; 35; 36; 40]. Additionally, more recent investigations have utilized contemporary machine learning models, like XGBoost and AdaBoost [37; 39]. As research in this domain progressed, it facilitated the development of larger datasets and the training of DL models, which have become prevalent in addressing modern problem-solving challenges [16; 38; 42; 46] \begin{table}
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**Paper** & **Strengths** & **Limitations** & \\ \hline [PERSON] and [PERSON] [13] & - & Primitive academic research on cryptocurrency P\&D schemes & - \\  & - & Unsupervised detection of P\&D schemes & - \\ \hline [PERSON] and [PERSON] [39] & - & Labeling pump events based on Telegram messages & - & Lack of a sufficient number of ML models for proper comparison of results one exchange \\ \hline [PERSON] and [PERSON] [15] & - & Extracting effective features for the input data & - & Lack of a confirmed P\&D database \\  & - & Detecting groups of traders that buy/sell together to detect possible P\&D schemes & - \\  & - & & - & Lilled scope: analysis restricted to a single cryptocurrency (BTC) \\  & - & P\&D schemes & - \\ \hline [PERSON] et al. [36] & - & Defining rush orders as a feature for input data & - & Limited scope: analysis just on one exchange \\  & - & - & Insufficient split size for test-set \\ \hline \hline \end{tabular}
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Table 4: Strengths and limitations of each study. \begin{table}
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Table 3: _Cont._Many of these studies lack a reliable P&D database. It is difficult to identify P&D instances without a dataset containing confirmed P&D events. There were limitations due to limited data availability in several studies [13; 15; 16; 35; 36; 37; 38; 39; 40; 42; 43; 45; 46]. A large amount of data are required for effective training of ML models. It is possible that insufficient and imbalanced data result in training models that lead to errors when predicting new data, which could potentially have a negative impact on the authors' conclusions and arguments. In addition, it is possible for models performing well with small datasets not to replicate the same results when applied to larger and more representative samples. As a result, the model's performance should always be evaluated using a substantial, adequate, and valid dataset. There is also a notable limitation in some studies for utilizing a limited number of models to address the issue [15; 36; 37; 39; 46]. Using only one model to predict P&D activities makes it difficult to rely on the results since there is no benchmark for comparing the model's performance. The use of multiple models for tackling a problem is not only beneficial in comparing their performance but also enhances confidence in their effectiveness. In addition, the use of a limited number of performance analysis criteria represents another limitation [43; 45]. The performance of a model cannot be adequately assessed by relying on a single metric. Using multiple criteria to evaluate the model's performance allows for more robust conclusions to be drawn. A further limitation is studying a limited