InSDN: A Novel SDN Intrusion Dataset

DC FieldValueLanguage
dc.contributor.authorElsayed, Mahmoud Said-
dc.contributor.authorLe-Khac, Nhien-An-
dc.contributor.authorJurcut, Anca Delia-
dc.date.accessioned2021-11-10T10:32:07Z-
dc.date.available2021-11-10T10:32:07Z-
dc.date.copyright2020 the Authorsen_US
dc.date.issued2020-09-08-
dc.identifier.citationIEEE Accessen_US
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10197/12615-
dc.description.abstractSoftware-Defined Network (SDN) has been developed to reduce network complexity through control and manage the whole network from a centralized location. Today, SDN is widely implemented in many data center’s network environments. Nevertheless, emerging technology itself can lead to many vulnerabilities and threats which are still challenging for manufacturers to address it. Therefore, deploying Intrusion Detection Systems (IDSs) to monitor malicious activities is a crucial part of the network architecture. Although the centralized view of the SDN network creates new opportunities for the implementation of IDSs, the performance of these detection techniques relies on the quality of the training datasets. Unfortunately, there are no publicly available datasets that can be used directly for anomaly detection systems applied in SDN networks. The majority of the published studies use non-compatible and outdated datasets, such as the KDD’99 dataset. This manuscript aims to generate an attack-specific SDN dataset and it is publicly available to the researchers. To the best of our knowledge, our work is one of the first solutions to produce a comprehensive SDN dataset to verify the performance of intrusion detection systems. The new dataset includes the benign and various attack categories that can occur in the different elements of the SDN platform. Further, we demonstrate the use of our proposed dataset by performing an experimental evaluation using eight popular machine-learning-based techniques for IDSs.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectDatasetsen_US
dc.subjectIntrusion detection system (IDS)en_US
dc.subjectOpenFlowen_US
dc.subjectSDNen_US
dc.subjectSecurityen_US
dc.subjectThreat vectorsen_US
dc.subjectMachine learningen_US
dc.titleInSDN: A Novel SDN Intrusion Dataseten_US
dc.typeJournal Articleen_US
dc.internal.authorcontactotheranca.jurcut@ucd.ieen_US
dc.statusPeer revieweden_US
dc.identifier.volume8en_US
dc.identifier.startpage165263en_US
dc.identifier.endpage165284en_US
dc.identifier.doi10.1109/access.2020.3022633-
dc.neeo.contributorElsayed|Mahmoud Said|aut|-
dc.neeo.contributorLe-Khac|Nhien-An|aut|-
dc.neeo.contributorJurcut|Anca Delia|aut|-
dc.date.updated2020-09-16T15:23:50Z-
dc.rights.licensehttps://creativecommons.org/licenses/by/3.0/ie/en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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