Difference between revisions of "Datasets"
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| + | This site summarizes different datasets gathered by the Robotics Group during their researches. | ||
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__FORCETOC__ | __FORCETOC__ | ||
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| + | [[File:Logo_feder.png|thumb|300px]] | ||
| + | [[File:MICINN_Gob_Web_AEI.jpg|thumb|300px]] | ||
== 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/ | + | === ([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 === |
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| + | 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. | ||
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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 === | ||
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| + | 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. | ||
| + | |||
| + | [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. | ||
| + | [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 === | ||
| + | |||
| + | 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" | ||
| − | + | [[File:Logo_feder.png|x60px]] | |
| + | [[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.
Contents
- 1 Available datasets
- 1.1 (RRID:SCR_015756) Benchmark dataset for analysis of cyber-attacks to an indoor real time localization system for autonomous robots
- 1.2 (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
- 1.3 (RRID:SCR_015743) Benchmark dataset for evaluation of range-based people tracker classifiers in mobile robots
- 1.4 NetFlow data for fitting malicious traffic detection models
- 1.5 Acknowledgement
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.
(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.
(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.
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"
