CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data
The CoAt-Set dataset is a transformed dataset specifically designed for collaborative anomaly detection within Collaborative Intrusion Detection Systems (CIDS). It is developed by extracting and relabeling coordinated attack patterns from well-established datasets, including CIC-ToN-IoT, CIC-IDS2017...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000861 |
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| _version_ | 1825199453234003968 |
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| author | Aulia Arif Wardana Grzegorz Kołaczek Parman Sukarno |
| author_facet | Aulia Arif Wardana Grzegorz Kołaczek Parman Sukarno |
| author_sort | Aulia Arif Wardana |
| collection | DOAJ |
| description | The CoAt-Set dataset is a transformed dataset specifically designed for collaborative anomaly detection within Collaborative Intrusion Detection Systems (CIDS). It is developed by extracting and relabeling coordinated attack patterns from well-established datasets, including CIC-ToN-IoT, CIC-IDS2017, CIC-UNSW-NB15, CSE-CIC-IDS2018, CIC-BoT-IoT, Distrinet-CIC-IDS2017, and NF-UQ-NIDS. CoAt-Set focuses on coordinated attack scenarios such as large-scale stealthy scans, worm outbreaks, and distributed denial-of-service (DDoS) attacks, simulating realistic and high-impact threats that commonly observed in modern networks. The transformation process involved organizing coordinated attack behaviors and providing detailed annotations and network traffic features, enhancing its relevance for anomaly detection in collaborative environments. CoAt-Set is compatible with standard machine learning frameworks, offering researchers and practitioners a comprehensive resource for developing, testing, and evaluating CIDS models. It is suitable for various applications, including collective threat intelligence research, analyzing distributed threat patterns, developing machine learning algorithms for distributed systems, and training simulations designed for heterogeneous network environments. |
| format | Article |
| id | doaj-art-d38c12a609234253b33bf715030032be |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-d38c12a609234253b33bf715030032be2025-02-08T05:00:35ZengElsevierData in Brief2352-34092025-04-0159111354CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley DataAulia Arif Wardana0Grzegorz Kołaczek1Parman Sukarno2Wrocław University of Science and Technology, Poland; Corresponding author.Wrocław University of Science and Technology, PolandTelkom University, IndonesiaThe CoAt-Set dataset is a transformed dataset specifically designed for collaborative anomaly detection within Collaborative Intrusion Detection Systems (CIDS). It is developed by extracting and relabeling coordinated attack patterns from well-established datasets, including CIC-ToN-IoT, CIC-IDS2017, CIC-UNSW-NB15, CSE-CIC-IDS2018, CIC-BoT-IoT, Distrinet-CIC-IDS2017, and NF-UQ-NIDS. CoAt-Set focuses on coordinated attack scenarios such as large-scale stealthy scans, worm outbreaks, and distributed denial-of-service (DDoS) attacks, simulating realistic and high-impact threats that commonly observed in modern networks. The transformation process involved organizing coordinated attack behaviors and providing detailed annotations and network traffic features, enhancing its relevance for anomaly detection in collaborative environments. CoAt-Set is compatible with standard machine learning frameworks, offering researchers and practitioners a comprehensive resource for developing, testing, and evaluating CIDS models. It is suitable for various applications, including collective threat intelligence research, analyzing distributed threat patterns, developing machine learning algorithms for distributed systems, and training simulations designed for heterogeneous network environments.http://www.sciencedirect.com/science/article/pii/S2352340925000861CybersecurityAnomaly detectionNetwork simulationAugmented dataHeterogeneous data |
| spellingShingle | Aulia Arif Wardana Grzegorz Kołaczek Parman Sukarno CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data Data in Brief Cybersecurity Anomaly detection Network simulation Augmented data Heterogeneous data |
| title | CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data |
| title_full | CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data |
| title_fullStr | CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data |
| title_full_unstemmed | CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data |
| title_short | CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data |
| title_sort | coat set transformed coordinated attack dataset for collaborative intrusion detection simulationmendeley data |
| topic | Cybersecurity Anomaly detection Network simulation Augmented data Heterogeneous data |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000861 |
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