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== Available datasets ==
 
== Available datasets ==
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Currently the following datasets are available:
 
Currently the following datasets are available:
  
=== [http://robotica.unileon.es/index.php/Benchmark_dataset_for_analysis_of_cyber-attacks_to_an_indoor_real_time_localization_system_for_autonomous_robots Benchmark dataset for analysis of cyber-attacks to an indoor real time localization system for autonomous robots] ===
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=== ([https://scicrunch.org/browse/resources/SCR_015756  RRID:SCR_015756]) Benchmark dataset for analysis of cyber-attacks to an indoor real time localization system for autonomous robots ===
  
 
This dataset can be used to analyze cyber-attacks to an indoor real time localization system for autonomous robots. Data have been gathered in an indoor mock-up apartmentlocated at the Robotics Lab of the University of León (Spain). An autonomous robot, called Karen, with an on-board Real Time Location System (RTLS) was used to gather the data.
 
This dataset can be used to analyze cyber-attacks to an indoor real time localization system for autonomous robots. Data have been gathered in an indoor mock-up apartmentlocated at the Robotics Lab of the University of León (Spain). An autonomous robot, called Karen, with an on-board Real Time Location System (RTLS) was used to gather the data.
  
=== [http://robotica.unileon.es/index.php/Benchmark_dataset_for_training/testing_of_Machine_Learning_Models_to_detect_cyber-attacks_to_an_indoor_real_time_localization_system_for_autonomous_robots Benchmark dataset for training/testing of Machine Learning Models to detect cyber-attacks to an indoor real time localization system for autonomous robots] ===
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[http://robotica.unileon.es/index.php/Benchmark_dataset_for_analysis_of_cyber-attacks_to_an_indoor_real_time_localization_system_for_autonomous_robots Go to dataset]
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=== ([https://scicrunch.org/browse/resources/SCR_015757 RRID:SCR_015757]) Benchmark dataset for training/testing of Machine Learning Models to detect cyber-attacks to an indoor real time localization system for autonomous robots ===
  
 
This dataset can be used to train and test Machine Learning Models to detect cyber-attacks  to an indoor real time localization system for autonomous robots. Data have been gathered in an indoor mock-up apartmentlocated at the Robotics Lab of the University of León (Spain). An autonomous robot, called Orbi-One, with an on-board Real Time Location System (RTLS) was used to gather the data.
 
This dataset can be used to train and test Machine Learning Models to detect cyber-attacks  to an indoor real time localization system for autonomous robots. Data have been gathered in an indoor mock-up apartmentlocated at the Robotics Lab of the University of León (Spain). An autonomous robot, called Orbi-One, with an on-board Real Time Location System (RTLS) was used to gather the data.
  
=== (RRID:SCR_015743) [http://robotica.unileon.es/index.php/Benchmark_dataset_for_evaluation_of_range-based_people_tracker_classifiers_in_mobile_robots Benchmark dataset for evaluation of range-based people tracker classifiers in mobile robots] ===
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[http://robotica.unileon.es/index.php/Benchmark_dataset_for_training/testing_of_Machine_Learning_Models_to_detect_cyber-attacks_to_an_indoor_real_time_localization_system_for_autonomous_robots  Go to dataset]
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=== ([https://scicrunch.org/browse/resources/SCR_015743 RRID:SCR_015743]) Benchmark dataset for evaluation of range-based people tracker classifiers in mobile robots ===
  
 
This dataset can be used to evaluate the performance of different approaches for detecting and tracking people by using lidar sensors. Information contained at the dataset is specially suitable to be used as training data for neural network-based classifiers.
 
This dataset can be used to evaluate the performance of different approaches for detecting and tracking people by using lidar sensors. Information contained at the dataset is specially suitable to be used as training data for neural network-based classifiers.
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[http://robotica.unileon.es/index.php/Benchmark_dataset_for_evaluation_of_range-based_people_tracker_classifiers_in_mobile_robots Go to dataset]
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=== NetFlow data for fitting malicious traffic detection models ===
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We have gathered 2 datasets for malicious traffic detection, specifically port-scanning attacks. Both D1 and D2, contain approximately 50% benign flow data and 50% malicious flow data. Benign flow data, corresponding to legitimate network traffic, has been labeled as `0'. Malicious flow data, corresponding to port-scanning attacks, has been labeled as `1'. Both datasets are freely available online under an open-access license.
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[https://doi.org/10.5281/zenodo.4106730 D1, doi:10.5281/zenodo.4106730]
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[https://doi.org/10.5281/zenodo.4106738 D2, doi:10.5281/zenodo.4106738]
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=== Acknowledgement ===
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This material is based upon work supported by the
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"Proyecto RTI2018-100683-B-I00, financiado por: FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación"
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[[File:Logo_feder.png|x60px]]
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[[File:MICINN_Gob_Web_AEI.jpg|x60px]]

Latest revision as of 09:41, 9 March 2022

This site summarizes different datasets gathered by the Robotics Group during their researches.


Logo feder.png
MICINN Gob Web AEI.jpg

Available datasets

Currently the following datasets are available:

(RRID:SCR_015756) Benchmark dataset for analysis of cyber-attacks to an indoor real time localization system for autonomous robots

This dataset can be used to analyze cyber-attacks to an indoor real time localization system for autonomous robots. Data have been gathered in an indoor mock-up apartmentlocated at the Robotics Lab of the University of León (Spain). An autonomous robot, called Karen, with an on-board Real Time Location System (RTLS) was used to gather the data.

Go to dataset

(RRID:SCR_015757) Benchmark dataset for training/testing of Machine Learning Models to detect cyber-attacks to an indoor real time localization system for autonomous robots

This dataset can be used to train and test Machine Learning Models to detect cyber-attacks to an indoor real time localization system for autonomous robots. Data have been gathered in an indoor mock-up apartmentlocated at the Robotics Lab of the University of León (Spain). An autonomous robot, called Orbi-One, with an on-board Real Time Location System (RTLS) was used to gather the data.

Go to dataset

(RRID:SCR_015743) Benchmark dataset for evaluation of range-based people tracker classifiers in mobile robots

This dataset can be used to evaluate the performance of different approaches for detecting and tracking people by using lidar sensors. Information contained at the dataset is specially suitable to be used as training data for neural network-based classifiers.

Go to dataset

NetFlow data for fitting malicious traffic detection models

We have gathered 2 datasets for malicious traffic detection, specifically port-scanning attacks. Both D1 and D2, contain approximately 50% benign flow data and 50% malicious flow data. Benign flow data, corresponding to legitimate network traffic, has been labeled as `0'. Malicious flow data, corresponding to port-scanning attacks, has been labeled as `1'. Both datasets are freely available online under an open-access license.

D1, doi:10.5281/zenodo.4106730

D2, doi:10.5281/zenodo.4106738

Acknowledgement

This material is based upon work supported by the "Proyecto RTI2018-100683-B-I00, financiado por: FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación"

Logo feder.png MICINN Gob Web AEI.jpg