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In this paper, we focus on predicting the pump probability of all coins listed in the target exchange before a scheduled pump time, which we refer to as the target coin prediction task. Firstly, we conduct a comprehensive study of the latest 709 P&amp;D events organized in Telegram from Jan. 2019 to Jan. 2022. Our empirical analysis reveals some interesting patterns of P&amp;Ds, such as that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity. Here channel refers a form of group in Telegram that is frequently used to coordinate P&amp;D events. This observation inspires us to develop a novel sequence-based neural network, dubbed SNN, which encodes a channel's P&amp;D event history into a sequence representation via the positional attention mechanism to enhance the prediction accuracy. Positional attention helps to extract useful information and alleviates noise, especially when the sequence length is long. Extensive experiments verify the effectiveness and generalizability of proposed methods. Additionally, we release the code and P&amp;D dataset on GitHub https:\/\/github.com\/Bayi-Hu\/Pump-and-Dump-Detection-on-Cryptocurrency, and regularly update the dataset.<\/jats:p>","DOI":"10.1145\/3588686","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T17:42:05Z","timestamp":1685468525000},"page":"1-19","source":"Crossref","is-referenced-by-count":15,"title":["Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3297-6991","authenticated-orcid":false,"given":"Sihao","family":"Hu","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5769-8786","authenticated-orcid":false,"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4775-4005","authenticated-orcid":false,"given":"Shengliang","family":"Lu","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8618-4581","authenticated-orcid":false,"given":"Bingsheng","family":"He","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5056-0351","authenticated-orcid":false,"given":"Zhao","family":"Li","sequence":"additional","affiliation":[{"name":"Link2do Technology, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"Binance kline api. https:\/\/data.binance.vision\/?prefix=data\/spot\/monthly\/klines."},{"key":"e_1_2_2_2_1","unstructured":"Coingecko api. https:\/\/www.coingecko.com\/en\/api."},{"key":"e_1_2_2_3_1","unstructured":"Pumpolymp. https:\/\/pumpolymp.com."},{"key":"e_1_2_2_4_1","unstructured":"Telethon api. https:\/\/docs.telethon.dev."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336499.3338003"},{"key":"e_1_2_2_6_1","volume-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271","author":"Bai S.","year":"2018","unstructured":"S. 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