Use of Traffic Sampling in Anomaly Detection for High-Throughput Network Links

Currently, anomaly detection is an increasingly important issue in terms of research work and applications in production systems. Information about system malfunction allows the implementation of precise diagnostic and corrective actions. Two main approaches based on statistical analysis and machine learning techniques are used in anomaly detection systems, which are computationally complex, especially when dealing with high traffic volumes in computer network. In this paper, the limitation of the sampling frequency for network traffic parameters is proposed as a technique to reduce the computational complexity of anomaly detection methods. The proposed approach has been verified in a real network link monitoring system for a medium-sized ISP. The results obtained are promising and can be used to build a production system that enables the development of early warning systems in the area of security incident detection dedicated to high-speed access links.