Chapter
May 18, 2017
Detection of Cyber-Attacks to Water Systems through Machine-Learning-Based Anomaly Detection in SCADA Data
Authors: Sarin E. Chandy [email protected], Amin Rasekh, Ph.D. [email protected], Zachary A. Barker [email protected], Bruce Campbell [email protected], and M. Ehsan Shafiee, Ph.D. [email protected]Author Affiliations
Publication: World Environmental and Water Resources Congress 2017
Abstract
Vulnerability of water infrastructure to cyber-attacks is proliferated as the operation of these systems is automated using networked systems. Reports of several cyber-attacks to water systems such as the 2006 Pennsylvania Water Company hack and the 2012 Tijuana River sewage spill has proven the water community concerns for the security of these systems. This study proposes a machine-learning-based anomaly detection methodology for early cyber-attack detection and warning that is tailored to water distributions systems security. Attacks are assumed to be manifested as man-induced anomalies in SCADA time series data that should be detected in a short time with low false negative/positive rates. The methodology is trained by and examined on the BATtle of the Attack Detection ALgorithms (BATADAL) dataset.
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Published In
World Environmental and Water Resources Congress 2017
Pages: 611 - 616
Copyright
© 2017 American Society of Civil Engineers.
History
Published online: May 18, 2017
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Sensus Inc., 8601 Six Forks Rd., Suite 700, Raleigh, NC 27615. E-mail: [email protected]
Sensus Inc., 8601 Six Forks Rd., Suite 700, Raleigh, NC 27615. E-mail: [email protected]
Sensus Inc., 8601 Six Forks Rd., Suite 700, Raleigh, NC 27615. E-mail: [email protected]
Sensus Inc., 8601 Six Forks Rd., Suite 700, Raleigh, NC 27615. E-mail: [email protected]
Sensus Inc., 8601 Six Forks Rd., Suite 700, Raleigh, NC 27615. E-mail: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.