2024/09/27 by Josef Koumar, Koumar, Josef, Karel Hynek +5 · 5 citations
Computer Science · #Anomaly (physics) #Anomaly Detection Techniques and Applications #Anomaly detection #Artificial Intelligence (cs.AI) #Computer science #Data mining #FOS: Computer and information sciences #Geology #Machine Learning (cs.LG) #Machine learning #Network Security and Intrusion Detection #Series (stratigraphy) #Time Series Analysis and Forecasting #Time series
paper · pdf · doi:10.48550/arxiv.2409.18874
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Anomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. One of the primary approaches to anomaly detection are methods based on forecasting. Nevertheless, extensive real-world network datasets for forecasting and anomaly detection techniques are missing, potentially causing performance overestimation of anomaly detection algorithms. This manuscript addresses this gap by introducing a dataset comprising time series data of network entities' behavior, collected from the CESNET3 network. The dataset was created from 40 weeks of network traffic of 275 thousand active IP addresses. The ISP origin of the presented data ensures a high level of variability among network entities, which forms a unique and authentic challenge for forecasting and anomaly detection models. It provides valuable insights into the practical deployment of forecast-based anomaly detection approaches.