Deep Blockchain Approach for Anomaly Detection in the Bitcoin Network

Abstract

Long-term research has been done on anomaly detection. Its uses in the banking industry have made it easier to spot questionable hacker activity. However, it is more difficult to trick financial systems due to innovations in the financial sector like blockchain and artificial intelligence. Despite these technical developments, there have nevertheless been several instances of fraud. To address the anomaly detection issue, a variety of artificial intelligence algorithms have been put forth; while some findings seem to be remarkably encouraging, no clear victor has emerged. The transactional data of "Bitcoin," which is one of the public financial block chains, can be detected with the help of several anomaly detection algorithms. This paper makes a quantum leap toward bridging the gap between artificial intelligence and blockchain. In light of anomaly detection, this article also explains the importance of block chain technology and its use in the financial sector. Additionally, it pulls the bitcoin blockchain's transactional data and uses unsupervised machine learning algorithms to look for fraudulent transactions. Although various artificial intelligence algorithms have been proposed for anomaly detection, none have distinctly outperformed the rest. This paper delves into the intersection of artificial intelligence and blockchain, specifically focusing on the transactional data of Bitcoin, a leading public financial blockchain. We employ a range of unsupervised machine learning algorithms to identify potentially fraudulent transactions. A variety of techniques are assessed and contrasted, including isolation forest, cluster-based local outlier factor (CBLOF), deep autoencoder networks, and ensemble approaches service. This paper underscores the synergy of blockchain technology and anomaly detection in the realm of financial security, establishing a comprehensive perspective on modern approaches to fraud detection.