Openly accessible

Improved hybrid approach for side-channel analysis using efficient convolutional neural network and dimensionality reduction

Mukhtar, Naila, Fournaris, Apostolos P, Khan, Tariq M, Dimopoulos, Charis and Kong, Yinan 2020, Improved hybrid approach for side-channel analysis using efficient convolutional neural network and dimensionality reduction, IEEE Access, vol. 8, pp. 184298-184311, doi: 10.1109/access.2020.3029206.

Attached Files
Name Description MIMEType Size Downloads

Title Improved hybrid approach for side-channel analysis using efficient convolutional neural network and dimensionality reduction
Author(s) Mukhtar, Naila
Fournaris, Apostolos P
Khan, Tariq MORCID iD for Khan, Tariq M orcid.org/0000-0002-7477-1591
Dimopoulos, Charis
Kong, Yinan
Journal name IEEE Access
Volume number 8
Start page 184298
End page 184311
Total pages 14
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2020
ISSN 2169-3536
Keyword(s) Side-channel attacks
machine learning analysis
elliptic curve security
embedded system security
Language eng
DOI 10.1109/access.2020.3029206
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30143989

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

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.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 16 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Tue, 13 Oct 2020, 14:44:07 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.