Abstract

Blockchain, an emerging and very important technology in the financial industry is facing many challenges especially security wise. The decentralized nature and characteristics of the blockchain makes it more difficult for conventional intrusion detection and prevention systems to identify and prevent fraudulent activities in real-time. This has posed serious challenges for fraud detection systems, thereby contributing to the wider attempts being made to ensure secure blockchain environments and build trust in cryptocurrency markets. This research hereby proposes an ensemble model approach to detect fraudulent cryptocurrency transaction. The proposed model will combine two deep learning algorithms namely, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM). The ensemble model consistently demonstrated high precision and at the same time ensured that the transactions that were labelled fraudulent were indeed captured as true, while sustaining high recall to identify most of the fraudulent activities. This work has shown that ensemble learning can generate a more robust and accurate fraud detection system rather than the conventional or single models and this makes the model more relevant in situations with highly imbalanced datasets like cryptocurrency transactions like blockchain.

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