Dataset:
The UNSW-NB15 dataset
The UNSW-NB15 dataset
| dc.date.accessioned | 2021-11-26T10:34:04Z | |
| dc.date.available | 2021-11-26T10:34:04Z | |
| dc.date.issued | 2019 | en_US |
| dc.description.abstract | The raw network packets of the UNSW-NB 15 dataset was created by the IXIA PerfectStorm tool in the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) for generating a hybrid of real modern normal activities and synthetic contemporary attack behaviours. Tcpdump tool is utilised to capture 100 GB of the raw traffic (e.g., Pcap files). This data set has nine types of attacks, namely, Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode and Worms. The Argus, Bro-IDS tools are used and twelve algorithms are developed to generate totally 49 features with the class label. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1959.4/resource/collection/resdatac_445/1 | |
| dc.language | English | |
| dc.language.iso | EN | en_US |
| dc.rights | GPL | en_US |
| dc.rights.uri | https://www.gnu.org/licenses/gpl-3.0.html | en_US |
| dc.subject.other | UNSW-NB15 dataset | en_US |
| dc.title | The UNSW-NB15 dataset | en_US |
| dc.type | Dataset | en_US |
| dcterms.accessRights | open access | |
| dcterms.accrualMethod | Design a decent dataset for evaluating network anomaly detection system. | en_US |
| dcterms.accrualMethod | https://research.unsw.edu.au/projects/unsw-nb15-dataset | |
| dcterms.rights | Free use of the UNSW-NB15 dataset for academic research purposes is hereby granted in perpetuity. Use for commercial purposes is strictly prohibited. Nour Moustafa and Jill Slay have asserted their rights under the Copyright. | en_US |
| dcterms.rightsHolder | Copyright 2015, University of New South Wales | en_US |
| dspace.entity.type | Dataset | en_US |
| unsw.accessRights.uri | https://purl.org/coar/access_right/c_abf2 | |
| unsw.contributor.researchDataCreator | Moustafa, Nour | en_US |
| unsw.coverage.temporalFrom | 2015-09-30 | en_US |
| unsw.description.storageURL | https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/ | en_US |
| unsw.description.storageURL | https://cloudstor.aarnet.edu.au/plus/index.php/s/2DhnLGDdEECo4ys | en_US |
| unsw.description.storageURL | https://research.unsw.edu.au/projects/unsw-nb15-dataset | en_US |
| unsw.identifier.doi | https://doi.org/10.26190/5d7ac5b1e8485 | en_US |
| unsw.isPublicationRelatedToDataset | http://ro.ecu.edu.au/isw/59/ | en_US |
| unsw.isPublicationRelatedToDataset | http://www.tandfonline.com/doi/abs/10.1080/19393555.2015.1125974 | en_US |
| unsw.isPublicationRelatedToDataset | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7348942 | en_US |
| unsw.isPublicationRelatedToDataset | Deep Neural Networks for Network Intrusion Detection | |
| unsw.relation.OriginalPublicationAffiliation | Moustafa, Nour, Sch of Engineering & IT (Sum), UNSW Canberra, | en_US |
| unsw.relation.faculty | UNSW Canberra | |
| unsw.relation.fundingAgency | school of Engineering and Information Technology- Canberra | en_US |
| unsw.relation.fundingScheme | A funding for a PhD scheme, relates to design an effective anomaly detection system | en_US |
| unsw.relation.projectDesc | A TFS scholarship for a student PhD to designing novel statitcal anomaly Detection system, be able to apply online and adaptive with detecting zero-day attacks | en_US |
| unsw.relation.projectEndDate | 2017-05-03 | en_US |
| unsw.relation.projectStartDate | 2014-08-07 | en_US |
| unsw.relation.projectTitle | A TFS scholarship for a student PhD to designing novel anomaly Detection system | en_US |
| unsw.relation.school | School of Engineering and Information Technology | |
| unsw.subject.SEOcode | 940301 Defence and Security Policy | en_US |
| unsw.subject.fieldofresearchcode | 080303 Computer System Security | en_US |