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.

The dataset descriptor paper presents an essential and structured documentation of the Internet Protocol Stack Dataset, offering a comprehensive overview of its contents and potential applications in the field of networking and telecommunications. This descriptor paper details the dataset’s organization into tunable parameters, non-tunable parameters, and performance metrics for VoIP and HTTP sessions, providing insights into its significance for network performance evaluation. Researchers, network engineers, and data scientists can utilize this descriptor as a guide to effectively access and utilize the dataset for various purposes, including network optimization, quality of service assessment, and network modeling, ultimately contributing to advancements in network performance and infrastructure development.Â