Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks
Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefor...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1528 |
| _version_ | 1797395574986637312 |
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| author | Ismael Essop José C. Ribeiro Maria Papaioannou Georgios Zachos Georgios Mantas Jonathan Rodriguez |
| author_facet | Ismael Essop José C. Ribeiro Maria Papaioannou Georgios Zachos Georgios Mantas Jonathan Rodriguez |
| author_sort | Ismael Essop |
| collection | DOAJ |
| description | Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin “powertrace” and the Cooja tool “Radio messages”. |
| first_indexed | 2024-03-09T00:37:29Z |
| format | Article |
| id | doaj.art-6e976119db9b422397014ace282b4b14 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:37:29Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6e976119db9b422397014ace282b4b142023-12-11T18:04:12ZengMDPI AGSensors1424-82202021-02-01214152810.3390/s21041528Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT NetworksIsmael Essop0José C. Ribeiro1Maria Papaioannou2Georgios Zachos3Georgios Mantas4Jonathan Rodriguez5Faculty of Engineering and Science, University of Greenwich, Chatham Maritime ME4 4TB, UKInstituto de Telecomunicações, Aveiro 3810-193, PortugalFaculty of Engineering and Science, University of Greenwich, Chatham Maritime ME4 4TB, UKFaculty of Engineering and Science, University of Greenwich, Chatham Maritime ME4 4TB, UKFaculty of Engineering and Science, University of Greenwich, Chatham Maritime ME4 4TB, UKInstituto de Telecomunicações, Aveiro 3810-193, PortugalOver the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin “powertrace” and the Cooja tool “Radio messages”.https://www.mdpi.com/1424-8220/21/4/1528IoTIndustrial IoTbenign datasets generationmalicious datasets generationCooja simulatorContiki OS |
| spellingShingle | Ismael Essop José C. Ribeiro Maria Papaioannou Georgios Zachos Georgios Mantas Jonathan Rodriguez Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks Sensors IoT Industrial IoT benign datasets generation malicious datasets generation Cooja simulator Contiki OS |
| title | Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks |
| title_full | Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks |
| title_fullStr | Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks |
| title_full_unstemmed | Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks |
| title_short | Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks |
| title_sort | generating datasets for anomaly based intrusion detection systems in iot and industrial iot networks |
| topic | IoT Industrial IoT benign datasets generation malicious datasets generation Cooja simulator Contiki OS |
| url | https://www.mdpi.com/1424-8220/21/4/1528 |
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