Detecting anomalies in large-scale network traffic is one of the pressing issues in modern information security. The volume of traffic generated as a result of the expansion of Internet services, cloud computing, the development of IoT and 5G networks is increasing dramatically, and this process reduces the effectiveness of traditional security mechanisms. This article studies and compares statistical methods and machine learning (ML) approaches to detect anomalous behavior in the network. The advantages of statistical approaches, including Z-score, Chebyshev inequality, analysis of variance and time series models, are explained by their fast performance and efficiency in real-time monitoring, but their accuracy is limited in large-scale data. Machine learning methods (Random Forest, SVM, Neural networks, K-means, DBSCAN, Autoencoder) provide high accuracy and flexibility, but they are computationally intensive. The results of the study show that a hybrid approach - integrating statistical and ML methods - can significantly increase efficiency.