A survey on security threats and defensive techniques of machine learning: a data driven view Liu, Qiang, Li, Pan, Zhao, Wentao, Cai, Wei, Yu, Shui and Leung, Victor CM 2018, A survey on security threats and defensive techniques of machine learning: a data driven view, IEEE access, vol. 6, pp. 12103-12117, doi: 10.1109/ACCESS.2018.2805680. Attached Files Name Description MIMEType Size Downloads Title A survey on security threats and defensive techniques of machine learning: a data driven view Author(s) Liu, Qiang Li, Pan Zhao, Wentao Cai, Wei Yu, Shui orcid.org/0000-0003-4485-6743 Leung, Victor CM Journal name IEEE access Volume number 6 Start page 12103 End page 12117 Total pages 15 Publisher Institute of Electrical and Electronics Engineers Place of publication Piscataway, N.J. Publication date 2018-02-13 ISSN 2169-3536 Keyword(s) machine learning adversarial samples security threats defensive techniques Language eng DOI 10.1109/ACCESS.2018.2805680 HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2018, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30106991 Document type: Journal Article Collection: School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 22 Mar 2018, 14:19:26 EST Fri, 23 Mar 2018, 04:00:14 EST Fri, 23 Mar 2018, 07:10:15 EST Tue, 27 Mar 2018, 05:03:57 EST Tue, 27 Mar 2018, 11:19:25 EST Tue, 27 Mar 2018, 11:20:54 EST Tue, 27 Mar 2018, 11:21:32 EST Sat, 21 Apr 2018, 02:30:48 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 84 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Thu, 22 Mar 2018, 14:19:26 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.