Isolation Forest based Fraud Detection for Cryptocurrency Transaction

The growing popularity of cryptocurrencies has brought attention to the need for effective fraud detection mechanisms within this dynamic and rapidly evolving domain. This research explores the application of the Isolation Forest algorithm for detecting fraudulent transactions within cryptocurrency networks. The Isolation Forest algorithm is particularly well-suited for identifying anomalies in datasets characterized by imbalanced classes or rare instances, making it an ideal candidate for detecting infrequent fraudulent activities in cryptocurrency transactions. The proposed methodology involves preparing a dataset of cryptocurrency transactions, including features such as transaction amounts, timestamps, and wallet addresses. By labelling the data with known legitimate and fraudulent transactions, the Isolation Forest model is trained to recognize patterns that differentiate between the two classes. During training, the algorithm constructs a forest of isolation trees, and each transaction’s anomaly score is determined based on its average path length through these trees. A critical step involves setting a suitable threshold above which a transaction is deemed anomalous. This threshold is determined through rigorous experimentation and validation to strike a balance between minimizing false positives and capturing actual fraud cases. The trained Isolation Forest model is deployed in a real-time system, capable of processing incoming cryptocurrency transactions and promptly flagging potential instances of fraud. Performance evaluation of the model is conducted using relevant metrics such as precision, recall, and F1-score, on a separate validation or test dataset from which it is proved that the proposed methodology attains 6% performance improvement than the existing methodology.