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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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CubeSat cybersecurity dataset for intrusion detection (CuCD-ID): Labelled NOS3/cFS telemetry (raw + augmented) with

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This data article introduces the CubeSat Cybersecurity Dataset for Intrusion Detection (CuCD-ID), offering labeled space system data for machine learning security research. It supports developing robust intrusion detection models for CubeSats.

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Area of Science:

  • Space Systems Engineering
  • Cybersecurity
  • Machine Learning

Background:

  • Space systems face increasing cybersecurity threats.
  • Existing datasets may not adequately represent space-specific attack vectors.
  • Machine learning is crucial for detecting novel threats in real-time.

Purpose of the Study:

  • To present the CubeSat Cybersecurity Dataset for Intrusion Detection (CuCD-ID).
  • To provide a labeled dataset for developing and benchmarking machine learning-based intrusion detection systems for space missions.
  • To facilitate research into robust cybersecurity for CubeSats.

Main Methods:

  • Data generation using NASA's Operational Simulator for Small Satellites (NOS3) and core Flight System (cFS).
  • Simulated four adversarial tactics (command flooding, false data injection, storage exhaustion, defense impairment) based on the SPARTA framework.
  • Collected telemetry and command data via COSMOS v4, creating raw and augmented (noised) CSV datasets.

Main Results:

  • The CuCD-ID dataset comprises two tabular CSV files: a raw dataset (25,000 records, 31 features) and an augmented, noised dataset (22,465 records, 23 features).
  • Features include CCSDS packet header information, engineered sliding window metrics, system-level data, and intrusion labels.
  • The augmented dataset incorporates nine noise categories to enhance model robustness against in-orbit disturbances.

Conclusions:

  • The CuCD-ID dataset is valuable for developing and evaluating supervised and unsupervised intrusion detection methods.
  • It is suitable for on-board and Tiny Machine Learning applications in space systems.
  • Provided simulation scripts ensure reproducibility and enable further data expansion.