Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/57064
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yousefi-Azar, Mahmood | en |
dc.contributor.author | Varadharajan, Vijay | en |
dc.contributor.author | Hamey, Len | en |
dc.contributor.author | Tupakula, Uday | en |
dc.date.accessioned | 2023-12-21T02:26:42Z | - |
dc.date.available | 2023-12-21T02:26:42Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | IJCNN 2017 : the International Joint Conference on Neural Networks, p. 3854-3861, p. 3854-3861 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/57064 | - |
dc.description.abstract | <p>This paper presents a novel feature learning model for cyber security tasks. We propose to use Auto-encoders (AEs), as a generative model, to learn latent representation of different feature sets. We show how well the AE is capable of automatically learning a reasonable notion of semantic similarity among input features. Specifically, the AE accepts a feature vector, obtained from cyber security phenomena, and extracts a code vector that captures the semantic similarity between the feature vectors. This similarity is embedded in an abstract latent representation. Because the AE is trained in an unsupervised fashion, the main part of this success comes from appropriate original feature set that is used in this paper. It can also provide more discriminative features in contrast to other feature engineering approaches. Furthermore, the scheme can reduce the dimensionality of the features thereby signicantly minimising the memory requirements. We selected two different cyber security tasks: networkbased anomaly intrusion detection and Malware classication. We have analysed the proposed scheme with various classifiers using publicly available datasets for network anomaly intrusion detection and malware classifications. Several appropriate evaluation metrics show improvement compared to prior results.</p> | en |
dc.language | en | en |
dc.publisher | IEEE, Institute of Electrical and Electronics Engineers | en |
dc.relation.ispartof | IJCNN 2017 : the International Joint Conference on Neural Networks, p. 3854-3861 | en |
dc.title | Autoencoder-based feature learning for cyber security applications | en |
dc.type | Conference Publication | en |
dc.relation.conference | 2017 International Joint Conference on Neural Networks (IJCNN) | en |
dc.identifier.doi | 10.1109/IJCNN.2017.7966342 | en |
local.contributor.firstname | Mahmood | en |
local.contributor.firstname | Vijay | en |
local.contributor.firstname | Len | en |
local.contributor.firstname | Uday | en |
local.profile.school | School of Science & Technology | en |
local.profile.email | utupakul@une.edu.au | en |
local.output.category | E1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.date.conference | 14th -19th May, 2017 | en |
local.conference.place | Anchorage, Alaska, United States of America | en |
local.publisher.place | Piscataway, New Jersey, United States of America | en |
local.format.startpage | 3854 | en |
local.format.endpage | 3861 | en |
local.peerreviewed | Yes | en |
local.contributor.lastname | Yousefi-Azar | en |
local.contributor.lastname | Varadharajan | en |
local.contributor.lastname | Hamey | en |
local.contributor.lastname | Tupakula | en |
dc.identifier.staff | une-id:utupakul | en |
local.profile.orcid | 0000-0001-5048-9797 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:1959.11/57064 | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Autoencoder-based feature learning for cyber security applications | en |
local.output.categorydescription | E1 Refereed Scholarly Conference Publication | en |
local.relation.url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7965823 | en |
local.conference.details | 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, Alaska, United States of America, 14th -19th May, 2017 | en |
local.search.author | Yousefi-Azar, Mahmood | en |
local.search.author | Varadharajan, Vijay | en |
local.search.author | Hamey, Len | en |
local.search.author | Tupakula, Uday | en |
local.uneassociation | No | en |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.year.published | 2017 | en |
local.subject.for2020 | 460407 System and network security | en |
local.subject.seo2020 | 220405 Cybersecurity | en |
local.date.start | 2017-05-14 | - |
local.date.end | 2017-05-19 | - |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.relation.worldcat | https://www.worldcat.org/search?q=978-1-5090-6183-9 | en |
Appears in Collections: | Conference Publication School of Science and Technology |
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