Unmanned Aerial Vehicle (UAV) Intrusion Detection
Donated on 4/11/2020
For UAV identification, each input is an encrypted WiFi traffic record while the output is whether the current traffic is from a UAV or not. Meta-info on attribute relationship is also provided.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Real
# Instances
17256
# Features
55
Dataset Information
Additional Information
Beyond traditional classification task, this dataset also contains other meta-information that help enable additional machine learning tasks. For example, this dataset contains the computational generation time for each statistical attributes, which is recorded in the diagonal values of the matrix D. It also contains the computational dependency among different attributes, which is denoted by the incidence matrix H. For example, the computation of standard deviation contains the computation of mean. Detailed information on D and H are as follows: D: k× 1. The generation runtime for each feature. H: k'×k. The incident matrix of the feature computational hypergraph (see the above paper for details). k' is the number of feature computational components and k is the numbe of features. More information is included in the source paper.
Has Missing Values?
No
Variable Information
The raw inputs are the radio frequency time series in two directions: uplink_flow and downlink_flow. Attributes are processed from the raw input, the list of attributes are: 1. uplink_size_mean 2. uplink_size_median 3. uplink_size_MAD 4. uplink_size_STD 5. uplink_size_Skewness 6. uplink_size_Kurtosis 7. uplink_size_MAX 8. uplink_size_MIN 9. uplink_size_MeanSquare 10. downlink_size_mean 11. downlink_size_median 12. downlink_size_MAD 13. downlink_size_STD 14. downlink_size_Skewness 15. downlink_size_Kurtosis 16. downlink_size_MAX 17. downlink_size_MIN 18. downlink_size_MeanSquare 19. both_links_size_mean 20. both_links_size_median 21. both_links_size_MAD 22. both_links_size_STD 23. both_links_size_Skewness 24. both_links_size_Kurtosis 25. both_links_size_MAX 26. both_links_size_MIN 27. both_links_size_MeanSquare 28. uplink_interval_mean 29. uplink_interval_median 30. uplink_interval_MAD 31. uplink_interval_STD 32. uplink_interval_Skewness 33. uplink_interval_Kurtosis 34. uplink_interval_MAX 35. uplink_interval_MIN 36. uplink_interval_MeanSquare 37. downlink_interval_mean 38. downlink_interval_median 39. downlink_interval_MAD 40. downlink_interval_STD 41. downlink_interval_Skewness 42. downlink_interval_Kurtosis 43. downlink_interval_MAX 44. downlink_interval_MIN 45. downlink_interval_MeanSquare 46. both_links_interval_mean 47. both_links_interval_median 48. both_links_interval_MAD 49. both_links_interval_STD 50. both_links_interval_Skewness 51. both_links_interval_Kurtosis 52. both_links_interval_MAX 53. both_links_interval_MIN 54. both_links_interval_MeanSquare 55. label
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset unmanned_aerial_vehicle_uav_intrusion_detection = fetch_ucirepo(id=564) # data (as pandas dataframes) X = unmanned_aerial_vehicle_uav_intrusion_detection.data.features y = unmanned_aerial_vehicle_uav_intrusion_detection.data.targets # metadata print(unmanned_aerial_vehicle_uav_intrusion_detection.metadata) # variable information print(unmanned_aerial_vehicle_uav_intrusion_detection.variables)
Unmanned Aerial Vehicle (UAV) Intrusion Detection. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C56P6X.
@misc{misc_unmanned_aerial_vehicle_(uav)_intrusion_detection_564,
title = {{Unmanned Aerial Vehicle (UAV) Intrusion Detection}},
year = {2020},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C56P6X}
}DOI
Notes
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.