CESNET-TimeSeries24：用于网络流量异常检测与预测的时间序列数据集
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
AI总结：
本文提出CESNET-TimeSeries24数据集，基于CESNET3网络中27.5万个活跃IP地址40周的流量构建，旨在填补大规模真实网络数据空白，为基于预测的异常检测方法提供真实评估基准。
摘要：
网络流量中的异常检测对于维护计算机网络安全和识别恶意活动至关重要。基于预测的方法是异常检测的主要途径之一。然而，用于预测和异常检测技术的大规模真实网络数据集十分缺乏，这可能导致异常检测算法的性能被高估。本文通过引入一个包含网络实体行为时间序列数据的数据集来填补这一空白，该数据集采集自CESNET3网络。该数据集由27.5万个活跃IP地址在40周内的网络流量构建而成。所呈现数据的ISP来源确保了网络实体之间具有高度的变异性，这为预测和异常检测模型构成了独特且真实的挑战。它为基于预测的异常检测方法的实际部署提供了有价值的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
