Publication

Abstract : The practical applications of blockchains can far supersede the widely known trading and cryptocurrency realm. If any service provider is looking for a consistent, immutable, and multitenancy-supported ledger, then blockchain is the promising solution. Nowadays, social engineering attacks are prevalent. And the attackers deceive cryptocurrency traders. This work investigates various ensemble learning, neural network, and machine learning algorithms for fraud detection and identifies the best decision-making algorithm. It is observed that Adaptive Boosting (AdaBoost) algorithm outperforms with an accuracy of 98.92%. Further, the fraud detection module is integrated with an application developed for cryptocurrency transactions. Before a new transaction is committed to blockchain, The fraud detection module intervenes and alerts the user. We have also designed a test bed of deployable Peer-to-Peer (P2P) network to simulate cryptocurrency transaction.