Abstract
Historically, supervisory control and data acquisition (SCADA) systems have relied on obscurity to safeguard against attacks. Indeed, external attackers lacked knowledge about proprietary system designs and software to access systems and execute attacks. The trend to interconnect to the Internet and incorporate standardized protocols, however, has resulted in an increase in the attack surface – attackers can now target SCADA systems and proceed to impact the physical systems they control. Dynamical estimation can be used to identify anomalies and attempts to maliciously affect controlled physical systems. This paper describes an intrusion detection method based on the dynamical estimation of systems. A generic water pipeline system is modeled using state space equations, and a discrete-time Kalman filter is used to estimate operational characteristics for anomaly-based intrusion detection. The effectiveness of the method is evaluated against deception attacks that target the water pipeline system. A co-simulation that integrates computational fluid dynamics software and MATLAB/Simulink is employed to simulate attacks and develop detection schemes.
Chapter PDF
References
A. Cardenas, S. Amin, Z. Lin, Y. Huang, C. Huang and S. Sastry, Attacks against process control systems: Risk assessment, detection and response, Proceedings of the Sixth ACM Symposium on Information, Computer and Communications Security, pp. 355–366, 2011.
A. Cardenas, S. Amin and S. Sastry, Research challenges for the security of control systems, Proceedings of the Third USENIX Conference on Hot Topics in Security, article no. 6, 2008.
G. Dan and H. Sandberg, Stealth attacks and protection schemes for state estimators in power systems, Proceedings of the First IEEE Conference on Smart Grid Communications, pp. 214–219, 2010.
C. De Silva, Mechatronics: An Integrated Approach, CRC Press, Boca Raton, Florida, 2005.
Flowmaster, FlowmasterLink for MATLAB V2.0.1, Schaumburg, Illinois ( www.flowmaster.com/flowmaster_flowmasterlink_matlab.html ).
Flowmaster, Flowmaster V7 Overview, Schaumburg, Illinois ( www.flow master.com/flowmaster_overview.html ).
T. Kailath and H. Poor, Detection of stochastic processes, IEEE Transactions on Information Theory, vol. 44(6), pp. 2230–2259, 1998.
Y. Liu, P. Ning and M. Reiter, False data injection attacks against state estimation in electric power grids, Proceedings of the Sixteenth ACM Conference on Computer and Communications Security, pp. 21–32, 2009.
D. Miller, Internal Flow Systems, British Hydromechanics Research Association, Cranfield, England, 1990.
W. Rugh, Linear System Theory, Prentice Hall, Upper Saddle River, New Jersey, 1995.
D. Simon, Optimal State Estimation: Kalman, H ∞ and Nonlinear Approaches, John Wiley, Hoboken, New Jersey, 2006.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
Alajlouni, S., Rao, V. (2013). Anomaly Detection in Liquid Pipelines Using Modeling, Co-Simulation and Dynamical Estimation. In: Butts, J., Shenoi, S. (eds) Critical Infrastructure Protection VII. ICCIP 2013. IFIP Advances in Information and Communication Technology, vol 417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45330-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-45330-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45329-8
Online ISBN: 978-3-642-45330-4
eBook Packages: Computer ScienceComputer Science (R0)