LLD: A Low Latency Detection Solution to Thwart Cryptocurrency Pump & Dumps

Ahmad Sani Bello, Jens Schneider, Roberto Di Pietro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Pump and Dump schemes represent a threat to any market. While this issue has long been regulated in mature markets, in unregulated markets, such as crypto exchanges, this plague is very present, and even exacerbated by the low capitalizaton of many cryptocurrencies that represent the perfect target for such a fraudulent scheme. In this paper, we detail a Low Latency Detection solution (LLD) based on deep learning to automatically detect pump and dump activities on centralized cryptocurrency exchanges. We train a LSTM-based auto-encoder on BTC valuations, which can reliably be considered a proxy for regular trading-due to their larger capitalization. We use this auto-encoder to predict valuations on alt coins and use thresholding on a Gaussian tail condition to trigger detection. We argue that low latency detection is paramount for the practicality of such approaches. Unlike previous methods, our solution (LLD) detects the majority of pumps in less than five minutes (2.2 minutes on average) when using OHLCV data at one-minute resolution. In addition, we use social media data only to generate ground truths during testing. We show that in many cases a significant amount of the trade volume could have been saved had LLD been used to trigger trade suspension mechanisms. The idiosyncratic approach of our scheme, its sound rationale and viability, combined with the quality of achieved results-tested over an extensive experimental campaign-and the insights discussed in the paper also pave the way for further research in the field.
Original languageEnglish (US)
Title of host publication2023 IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9798350310191
DOIs
StatePublished - Jan 1 2023
Externally publishedYes

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