Anomaly Detection in Blockchain Transactions within the Metaverse Using Anomaly Detection Techniques - Henderi; Quba Siddique (Bright Institute, 2024)
The rapid expansion of blockchain technology and its integration into the Metaverse has brought about significant opportunities, but also new challenges, particularly in ensuring the security and integrity of transactions. This study explores the application of anomaly detection techniques, specifically the Isolation Forest algorithm, to identify unusual and potentially fraudulent transactions within a blockchain dataset. The analysis focuses on detecting anomalies across various transaction types, such as sales and scams, and regions including Asia and Africa. The dataset, comprising 78,600 transactions, revealed that 3,930 (approximately 5%) were flagged as anomalies.