# A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning
## 4 Findings
### Supervised Approach
To solve this problem, they suggested utilizing the AdaBoost model; this model showed stable precision and recall and an improved F1-score. Also, the results showed that increasing the chunk size causes an improvement of the F1-score. All the mentioned studies have used classic ML algorithms to detect P&D. However; some studies employed modern approaches, such as DL models, to predict P&D schemes. For instance, [PERSON] et al. [38], used the same input features as [37] in addition to two other features, namely pump index and symbol. They utilized CLSTM and an anomaly transformer as novel approaches to detect P&D schemes in cryptocurrencies. The CLSTM model is made of convolutional layers which find the spatial features; max-pooling layers; LSTM layers which find the temporal features; and finally, fully connected layers for final prediction. The anomaly transformer introduces two novelties: an anomaly attention module and a minimax optimization strategy. The anomaly attention module computes series association and prior association. Minimax optimization consists of two phases: during the minimize phase, the series association moves toward the prior association, and during the maximize phase, the series association moves toward the original input. The results demonstrated that these DL models outperform classic ML models and statistical methods for detecting such fraudulent activities, where the anomaly transformer with a chunk size of 15 s and F1-score of 93.6 showed the best performance. In another study, [PERSON] et al. [16] used market data consisting of OHLCV data and social data, which were collected from official Reddit accounts, Facebook pages, and Twitter accounts to train four types of models that detect P&D. Training, validation, and test sets contained 197, 55, and 54 pump events, respectively. They trained the models in different training situations in terms of the type of training data (social, financial, or both) and lookback time window. These models consisted of baseline (LR), CNN, Bidirectional LSTM, and CLSTM. The CNN model is a convolutional neural network that is widely used in DL and is powerful in feature detection. Bidirectional LSTM finds sequential dependencies in both directions in an input sequence. CLSTM demonstrated the most promising performance and consistency, while BLSTM models exhibited inconsistent performance across configurations. Besides DL models, there are other modern approaches, such as XGBoost, that can be utilized in the literature. For instance, [PERSON] and [PERSON] [39] utilized BTC trading pairs data from Binance and chat histories from Telegram P&D groups to label the data. They examined the characteristics of pump announcements, the timing of pumps, simultaneous announcements across channels, and the price action following a pump. Three types of pump events were identified: sustained pumps, short-term P&D events, and failed pumps. They developed an XGBoost model to detect P&D events automatically, and they achieved a high AUC of 0.995, demonstrating good sensitivity and specificity on the test set. They created a dataset with positive and negative ground truth samples and engineered features, such as volume bars, price change rates, and time differences. Applying the model to 172 coins, it identified 612 pump-like events, primarily in low-market capitalization cryptocurrencies, confirming its effectiveness in detecting suspicious activity. The unique aspects of this study are high-resolution data analysis, focusing on Binance, to explore profitability, medium-term impact, and the utilization of ground truth for detection.