Openly accessible

An investigation of performance analysis of anomaly detection techniques for big data in SCADA systems

Ahmed, Mohiuddin, Anwar, Adnan, Mahmood, Abdun Naser, Shah, Zubair and Maher, Michael J 2015, An investigation of performance analysis of anomaly detection techniques for big data in SCADA systems, EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 2, no. 3, pp. 1-16, doi: 10.4108/inis.2.3.e5.

Attached Files
Name Description MIMEType Size Downloads

Title An investigation of performance analysis of anomaly detection techniques for big data in SCADA systems
Author(s) Ahmed, Mohiuddin
Anwar, AdnanORCID iD for Anwar, Adnan orcid.org/0000-0003-3916-1381
Mahmood, Abdun Naser
Shah, Zubair
Maher, Michael J
Journal name EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
Volume number 2
Issue number 3
Article ID e5
Start page 1
End page 16
Total pages 16
Publisher European Alliance for Innovation
Place of publication Gent, Belgium
Publication date 2015
ISSN 2410-0218
Keyword(s) Anomaly detection
SCADA systems
big data
Language eng
DOI 10.4108/inis.2.3.e5
Indigenous content off
HERDC Research category C1.1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30146894

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: 61 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Tue, 12 Jan 2021, 14:52:54 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.