Unified Spacecraft Anomaly Detection Benchmark Dataset

Anomaly detection plays a crucial role in various domains, including but not limited to cybersecurity, space science, finance, and healthcare. However, the lack of standardized benchmark datasets hinders the comparative evaluation of anomaly detection algorithms. In this work, we address this gap by presenting a curated collection of preprocessed datasets for spacecraft anomalies sourced from multiple sources. These datasets cover a diverse range of anomalies and real-world scenarios for the spacecrafts. Furthermore, we have added two general datsets ensuring comprehensive evaluation and generalizability of anomaly detection algorithms. Our compilation process involves rigorous preprocessing steps to ensure data integrity and privacy protection. Each dataset is thoroughly documented, including descriptions of anomalies, preprocessing methodologies, and evaluation metrics. By providing this unified benchmark dataset, we aim to facilitate fair and transparent evaluation of anomaly detection algorithms, ultimately advancing the state-of-the-art in anomaly detection research.

异常检测在众多领域扮演着至关重要的角色，诸如但不限于网络安全、空间科学、金融以及医疗保健等领域。然而，缺乏标准化的基准数据集阻碍了对异常检测算法的比较性评估。在本研究中，我们旨在填补这一空白，通过呈现一个精心挑选的、由多个来源收集的、针对航天器异常的预处理数据集集合。这些数据集涵盖了航天器在多样化异常和真实世界场景中的丰富案例。此外，我们还加入了两个通用数据集，以确保对异常检测算法进行全面评估和泛化。我们的汇编过程涉及严格的预处理步骤，以确保数据的完整性和隐私保护。每个数据集都经过详细记录，包括异常描述、预处理方法以及评估指标。通过提供这一统一基准数据集，我们旨在促进异常检测算法的公平和透明评估，最终推动异常检测研究领域的尖端进展。