CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting

Abstract

Abstract Anomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. Most approaches to anomaly detection use methods based on forecasting. Extensive real-world network datasets for forecasting and anomaly detection techniques are missing, potentially causing overestimation of anomaly detection algorithm performance and fabricating the illusion of progress. This manuscript tackles this issue by introducing a comprehensive dataset derived from 40 weeks of traffic transmitted by 275,000 active IP addresses in the CESNET3 network—an ISP network serving approximately half a million customers daily. It captures the behavior of diverse network entities, reflecting the variability typical of an ISP environment. This variability provides a realistic and challenging environment for developing forecasting and anomaly detection models, enabling evaluations that are closer to real-world deployment scenarios. It provides valuable insights into the practical deployment of forecast-based anomaly detection approaches.