Detection of anomalous cryptocurrency transactions using neural networks and ontologies

The article explores an effective approach to detecting anomalies in cryptocurrency transactions using neural network models, including convolutional, deep, and gated recurrent units (GRUs), and compares their performance with other existing methods for identifying illicit transactions in cryptocurrency networks. A research of relevant studies is con-ducted in the fields of transaction data analysis in the cryptocurrency network, data visualization for transaction anal-ysis, and the use of computer vision techniques for detecting anomalous behavior. The subject area of the study is de-fined. The problem of detecting anomalies in cryptocurrency transactions is based on the fact that these transactions are pseudonymous, i.e. there are no direct indications of the identity of the sender and recipient. The relevance and contribution of this work lie in the development of a method capable of identifying anomalous transactions with high accuracy in near real-time. Experimental studies were conducted using a dataset of cryptocurrency transactions, apply-ing both neural and non-neural classifiers. The results are compared against existing approaches in the field. The exper-iments demonstrated that gated recurrent units outperformed other neural models in this task, achieving an accuracy of 0.94, precision of 0.95, recall of 0.93, and F1-score of 0.94, indicating the high effectiveness of the proposed model. Nonetheless, this approach showed slightly lower performance compared to traditional machine learning algorithms, such as optimized distributed gradient boosting. The novelty of the proposed approach lies in its use of statistical char-acteristics derived from the transaction graph, combined with deep learning and gradient boosting techniques. The ap-proach can be applied in the development of software tools for detecting illicit cryptocurrency transactions within in-formation security systems and digital forensics.