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DroNER: Dataset for drone named entity recognition.

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Summary
This summary is machine-generated.

This study introduces a new dataset of drone flight log messages for forensic analysis. The dataset, formatted in CoNLL and annotated with IOB2, aids in understanding drone operations.

Keywords:
Digital forensicsDrone datasetDrone entity recognitionDrone forensicsInfrastructureNER dataset

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Area of Science:

  • Computer Science
  • Digital Forensics
  • Data Science

Background:

  • Drone flight logs contain critical operational data.
  • Publicly available drone datasets offer opportunities for forensic research.
  • Standardized datasets are needed for advancing drone forensics.

Purpose of the Study:

  • To construct a comprehensive dataset of drone flight log messages.
  • To facilitate research in drone forensics and data analysis.
  • To provide a resource for training and testing forensic tools.

Main Methods:

  • Extracted and processed log messages from VTO Labs' drone image datasets.
  • Applied decryption, parsing, cleansing, and unique filtering techniques.
  • Annotated data using the IOB2 scheme with six entity types, resulting in CoNLL format.

Main Results:

  • Compiled a dataset of 1850 drone log messages from 12 DJI models.
  • Split the dataset into 1412 training and 438 testing messages based on drone models.
  • Observed average log message lengths of 6.5 globally, 6.6 for training, and 8.8 for testing.

Conclusions:

  • The created dataset is a valuable resource for drone forensic investigations.
  • The dataset supports the development and evaluation of forensic analysis techniques.
  • Further research can leverage this dataset to enhance drone security and accountability.