# A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning
## 4 Findings
### Unsupervised Approach  
Some of the studies focused on finding abnormalities in the input data and classifying them as possible P&D schemes while they are not associated with labeled data for training. For instance, [PERSON] et al. [43] proposed an algorithm to detect user groups that are potentially involved in P&D schemes. To validate their algorithm, they analyzed the leaked transaction history of the Mt. Gox Bitcoin exchange, and, using an improved Apriori algorithm, they identified several user groups engaging in synchronized buying or selling. This data contained 14 million records and 120,000 users with information about user ID, date, type, and the amount of each transaction. The authors discovered numerous abnormal trading behaviors and pricing patterns within these detected groups. For example, some users buy much more Bitcoin than they sell, and in reverse. There are also a considerable number of users that never buy or sell Bitcoin in the system. The authors suggested that some of these abnormalities could be linked to accounts controlled by the exchange itself for some special reason, for example, balancing the trading and providing liquidity. Some studies used anomaly detection to find abnormal patterns in the input data and mark them as possible P&D schemes. [PERSON] and [PERSON] [13] were the first ones to propose criteria to define a cryptocurrency P&D and utilized anomaly detection techniques to identify anomalous trading activities that could indicate potential P&D schemes. They used OHLCV data from symbol pairs obtained from five different exchanges through the CCXT library [44]. This study detected three kinds of anomalies to analyze the data: price anomaly, volume anomaly, and pump anomaly, which is defined as a co-occurrence of previous anomalies. They detected anomalies with three different sets of parameters. The results indicated that there are signals in the trading data that can help detect these fraudulent activities. This study suggested that more regulated exchanges are less vulnerable to P&D schemes. Furthermore, [PERSON] et al. [45] proposed a hybrid anomaly detection based on distance and density metrics to address the challenge of anomaly detection in time series data. An automatic threshold-setting method was developed by the authors for distance-based anomaly detection, as well as the introduction of a new metric called the density score for density-based anomaly detection. In conclusion, the authors discussed the challenges of contextual anomaly detection and outlined their approach to transforming the problem into a point anomaly detection task. According to their experiments, the hybrid approach outperformed both density-based and distance-based approaches at detecting P&D in top-ranked exchange pairs. Previous studies with unsupervised approaches used classic ML algorithms to detect possible P&D cases. DL can also be used to detect these cases. [PERSON] et al. [46] trained an LSTM-based auto-encoder on Bitcoin valuations using a low latency detection (LLD) framework based on DL. The auto-encoder was then used to predict valuations on altcoins. Since this study uses an unsupervised approach, they did not need ground truth labels for training the model, but for testing the model, they collected 55 confirmed pump events from Telegram groups to make this data relevant. They also retrieved OHLCV data and the number of trades as input features to the model. This model was trained on the data obtained one day before each of the 55 pumps. They also trained this model on the data from two and three days before the pumps, and the results showed that the model that was trained with data from one day before the pumps had the best performance compared to the other two models. Finally, they performed anomaly detection on the predicted and actual prices to find the anomalous points. The study highlighted the advantages of LLD, such as practical deployment, unsupervised training, and fast inference, while eliminating the need for future data.