PPFSCADA: Privacy preserving framework for SCADA data publishing

Fahad,A, Tari,Z, Almalawi,A, Goscinski,A, Khalil,I and Mahmood,A 2014, PPFSCADA: Privacy preserving framework for SCADA data publishing, Future generation computer systems, vol. 37, pp. 496-511, doi: 10.1016/j.future.2014.03.002.

Attached Files
Name Description MIMEType Size Downloads

Title PPFSCADA: Privacy preserving framework for SCADA data publishing
Author(s) Fahad,A
Journal name Future generation computer systems
Volume number 37
Start page 496
End page 511
Total pages 16
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-07
ISSN 0167-739X
Keyword(s) Data publishing
Data security
Privacy preservation
Science & Technology
Computer Science, Theory & Methods
Computer Science
Summary Supervisory Control and Data Acquisition (SCADA) systems control and monitor industrial and critical infrastructure functions, such as electricity, gas, water, waste, railway, and traffic. Recent attacks on SCADA systems highlight the need for stronger SCADA security. Thus, sharing SCADA traffic data has become a vital requirement in SCADA systems to analyze security risks and develop appropriate security solutions. However, inappropriate sharing and usage of SCADA data could threaten the privacy of companies and prevent sharing of data. In this paper, we present a privacy preserving strategy-based permutation technique called PPFSCADA framework, in which data privacy, statistical properties and data mining utilities can be controlled at the same time. In particular, our proposed approach involves: (i) vertically partitioning the original data set to improve the performance of perturbation; (ii) developing a framework to deal with various types of network traffic data including numerical, categorical and hierarchical attributes; (iii) grouping the portioned sets into a number of clusters based on the proposed framework; and (iv) the perturbation process is accomplished by the alteration of the original attribute value by a new value (clusters centroid). The effectiveness of the proposed PPFSCADA framework is shown through several experiments on simulated SCADA, intrusion detection and network traffic data sets. Through experimental analysis, we show that PPFSCADA effectively deals with multivariate traffic attributes, producing compatible results as the original data, and also substantially improving the performance of the five supervised approaches and provides high level of privacy protection. © 2014 Published by Elsevier B.V. All rights reserved.
Language eng
DOI 10.1016/j.future.2014.03.002
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071696

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in TR Web of Science
Scopus Citation Count Cited 9 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 200 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Mon, 23 Mar 2015, 13:12:14 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.