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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.

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Title PPFSCADA: Privacy preserving framework for SCADA data publishing
Author(s) Fahad,A
Tari,Z
Almalawi,A
Goscinski,A
Khalil,I
Mahmood,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
Internet
Privacy preservation
SCADA
Science & Technology
Technology
Computer Science, Theory & Methods
Computer Science
ALGORITHMS
SETS
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
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