TY - GEN
T1 - Water distribution systems analysis symposium-battle of the attack detection algorithms (BATADAL)
AU - Aghashahi, Mohsen
AU - Sundararajan, Raanju
AU - Pourahmadi, Mohsen
AU - Banks, M. Katherine
N1 - Publisher Copyright:
© ASCE.
PY - 2017
Y1 - 2017
N2 - With the transition of water distribution systems (WDSs) to smarter ones, intelligent water networks' elements, such as programmable logic controls (PLCs), sensors, valves and supervisory control and data acquisition systems (SCADAs) have played a more significant role. Like other computer-based technologies, these smart elements will make WDSs more vulnerable to malicious intrusions due to cyber-physical attacks. Regarding the vulnerability of more intelligent WDSs, it is necessary to devise and apply anomaly detection algorithms being able to detect intrusions to water networks with the fewest false alarms. For the battle of the attack detection algorithms (BATADAL), to detect the manipulated data, a spectral domain method as a pre-processing technique is implemented to extract the important characteristics of the observed time series data and make them independent of time. Then, a supervised machine learning technique is used to classify the data and obtain the intrusion detection.
AB - With the transition of water distribution systems (WDSs) to smarter ones, intelligent water networks' elements, such as programmable logic controls (PLCs), sensors, valves and supervisory control and data acquisition systems (SCADAs) have played a more significant role. Like other computer-based technologies, these smart elements will make WDSs more vulnerable to malicious intrusions due to cyber-physical attacks. Regarding the vulnerability of more intelligent WDSs, it is necessary to devise and apply anomaly detection algorithms being able to detect intrusions to water networks with the fewest false alarms. For the battle of the attack detection algorithms (BATADAL), to detect the manipulated data, a spectral domain method as a pre-processing technique is implemented to extract the important characteristics of the observed time series data and make them independent of time. Then, a supervised machine learning technique is used to classify the data and obtain the intrusion detection.
UR - http://www.scopus.com/inward/record.url?scp=85021427740&partnerID=8YFLogxK
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U2 - 10.1061/9780784480595.010
DO - 10.1061/9780784480595.010
M3 - Conference contribution
AN - SCOPUS:85021427740
T3 - World Environmental and Water Resources Congress 2017: International Perspectives, History and Heritage, Emerging Technologies, and Student Papers - Selected Papers from the World Environmental and Water Resources Congress 2017
SP - 101
EP - 108
BT - World Environmental and Water Resources Congress 2017
A2 - Dunn, Christopher N.
A2 - Van Weele, Brian
PB - American Society of Civil Engineers (ASCE)
T2 - 17th World Environmental and Water Resources Congress 2017
Y2 - 21 May 2017 through 25 May 2017
ER -